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Embedded prompt tuning: Towards enhanced calibration of pretrained models for medical images Med. Image Anal. (IF 10.7) Pub Date : 2024-07-04 Wenqiang Zu, Shenghao Xie, Qing Zhao, Guoqi Li, Lei Ma
Foundation models pre-trained on large-scale data have been widely witnessed to achieve success in various natural imaging downstream tasks. methods aim to adapt foundation models to new domains by updating only a small portion of parameters in order to reduce computational overhead. However, the effectiveness of these PEFT methods, especially in cross-domain few-shot scenarios, e.g., medical image
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A causal counterfactual graph neural network for arising-from-chair abnormality detection in parkinsonians Med. Image Anal. (IF 10.7) Pub Date : 2024-07-02 Xinlu Tang, Rui Guo, Chencheng Zhang, Xiaohua Qian
The arising-from-chair task assessment is a key aspect of the evaluation of movement disorders in Parkinson's disease (PD). However, common scale-based clinical assessment methods are highly subjective and dependent on the neurologist's expertise. Alternate automated methods for arising-from-chair assessment can be established based on quantitative susceptibility mapping (QSM) images with multiple-instance
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Deep Bayesian active learning-to-rank with relative annotation for estimation of ulcerative colitis severity Med. Image Anal. (IF 10.7) Pub Date : 2024-07-02 Takeaki Kadota, Hideaki Hayashi, Ryoma Bise, Kiyohito Tanaka, Seiichi Uchida
Automatic image-based severity estimation is an important task in computer-aided diagnosis. Severity estimation by deep learning requires a large amount of training data to achieve a high performance. In general, severity estimation uses training data annotated with discrete (i.e., quantized) severity labels. Annotating discrete labels is often difficult in images with ambiguous severity, and the annotation
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The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue Med. Image Anal. (IF 10.7) Pub Date : 2024-07-01 Philippe Weitz, Masi Valkonen, Leslie Solorzano, Circe Carr, Kimmo Kartasalo, Constance Boissin, Sonja Koivukoski, Aino Kuusela, Dusan Rasic, Yanbo Feng, Sandra Sinius Pouplier, Abhinav Sharma, Kajsa Ledesma Eriksson, Stephanie Robertson, Christian Marzahl, Chandler D. Gatenbee, Alexander R.A. Anderson, Marek Wodzinski, Artur Jurgas, Niccolò Marini, Manfredo Atzori, Henning Müller, Daniel Budelmann
The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset
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A geometric approach to robust medical image segmentation Med. Image Anal. (IF 10.7) Pub Date : 2024-06-29 Ainkaran Santhirasekaram, Mathias Winkler, Andrea Rockall, Ben Glocker
Robustness of deep learning segmentation models is crucial for their safe incorporation into clinical practice. However, these models can falter when faced with distributional changes. This challenge is evident in magnetic resonance imaging (MRI) scans due to the diverse acquisition protocols across various domains, leading to differences in image characteristics such as textural appearances. We posit
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Uncertainty-aware multiple-instance learning for reliable classification: Application to optical coherence tomography Med. Image Anal. (IF 10.7) Pub Date : 2024-06-27 Coen de Vente, Bram van Ginneken, Carel B. Hoyng, Caroline C.W. Klaver, Clara I. Sánchez
Deep learning classification models for medical image analysis often perform well on data from scanners that were used to acquire the training data. However, when these models are applied to data from different vendors, their performance tends to drop substantially. Artifacts that only occur within scans from specific scanners are major causes of this poor generalizability. We aimed to enhance the
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Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge Med. Image Anal. (IF 10.7) Pub Date : 2024-06-27 Yang Nan, Xiaodan Xing, Shiyi Wang, Zeyu Tang, Federico N Felder, Sheng Zhang, Roberta Eufrasia Ledda, Xiaoliu Ding, Ruiqi Yu, Weiping Liu, Feng Shi, Tianyang Sun, Zehong Cao, Minghui Zhang, Yun Gu, Hanxiao Zhang, Jian Gao, Pingyu Wang, Wen Tang, Pengxin Yu, Han Kang, Junqiang Chen, Xing Lu, Boyu Zhang, Michail Mamalakis, Francesco Prinzi, Gianluca Carlini, Lisa Cuneo, Abhirup Banerjee, Zhaohu Xing
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Medical image registration via neural fields Med. Image Anal. (IF 10.7) Pub Date : 2024-06-27 Shanlin Sun, Kun Han, Chenyu You, Hao Tang, Deying Kong, Junayed Naushad, Xiangyi Yan, Haoyu Ma, Pooya Khosravi, James S. Duncan, Xiaohui Xie
Image registration is an essential step in many medical image analysis tasks. Traditional methods for image registration are primarily optimization-driven, finding the optimal deformations that maximize the similarity between two images. Recent learning-based methods, trained to directly predict transformations between two images, run much faster, but suffer from performance deficiencies due to domain
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Vessel-targeted compensation of deformable motion in interventional cone-beam CT Med. Image Anal. (IF 10.7) Pub Date : 2024-06-26 Alexander Lu, Heyuan Huang, Yicheng Hu, Wojciech Zbijewski, Mathias Unberath, Jeffrey H. Siewerdsen, Clifford R. Weiss, Alejandro Sisniega
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Dual-stream multi-dependency graph neural network enables precise cancer survival analysis Med. Image Anal. (IF 10.7) Pub Date : 2024-06-26 Zhikang Wang, Jiani Ma, Qian Gao, Chris Bain, Seiya Imoto, Pietro Liò, Hongmin Cai, Hao Chen, Jiangning Song
Histopathology image-based survival prediction aims to provide a precise assessment of cancer prognosis and can inform personalized treatment decision-making in order to improve patient outcomes. However, existing methods cannot automatically model the complex correlations between numerous morphologically diverse patches in each whole slide image (WSI), thereby preventing them from achieving a more
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MIST: Multi-instance selective transformer for histopathological subtype prediction Med. Image Anal. (IF 10.7) Pub Date : 2024-06-26 Rongchang Zhao, Zijun Xi, Huanchi Liu, Xiangkun Jian, Jian Zhang, Zijian Zhang, Shuo Li
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CP-Net: Instance-aware part segmentation network for biological cell parsing Med. Image Anal. (IF 10.7) Pub Date : 2024-06-24 Wenyuan Chen, Haocong Song, Changsheng Dai, Zongjie Huang, Andrew Wu, Guanqiao Shan, Hang Liu, Aojun Jiang, Xingjian Liu, Changhai Ru, Khaled Abdalla, Shivani N Dhanani, Katy Fatemeh Moosavi, Shruti Pathak, Clifford Librach, Zhuoran Zhang, Yu Sun
Instance segmentation of biological cells is important in medical image analysis for identifying and segmenting individual cells, and quantitative measurement of subcellular structures requires further cell-level subcellular part segmentation. Subcellular structure measurements are critical for cell phenotyping and quality analysis. For these purposes, instance-aware part segmentation network is first
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Reducing annotating load: Active learning with synthetic images in surgical instrument segmentation Med. Image Anal. (IF 10.7) Pub Date : 2024-06-22 Haonan Peng, Shan Lin, Daniel King, Yun-Hsuan Su, Waleed M. Abuzeid, Randall A. Bly, Kris S. Moe, Blake Hannaford
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PneumoLLM: Harnessing the power of large language model for pneumoconiosis diagnosis Med. Image Anal. (IF 10.7) Pub Date : 2024-06-20 Meiyue Song, Jiarui Wang, Zhihua Yu, Jiaxin Wang, Le Yang, Yuting Lu, Baicun Li, Xue Wang, Xiaoxu Wang, Qinghua Huang, Zhijun Li, Nikolaos I. Kanellakis, Jiangfeng Liu, Jing Wang, Binglu Wang, Juntao Yang
The conventional pretraining-and-finetuning paradigm, while effective for common diseases with ample data, faces challenges in diagnosing data-scarce occupational diseases like pneumoconiosis. Recently, large language models (LLMs) have exhibits unprecedented ability when conducting multiple tasks in dialogue, bringing opportunities to diagnosis. A common strategy might involve using adapter layers
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DIAS: A dataset and benchmark for intracranial artery segmentation in DSA sequences Med. Image Anal. (IF 10.7) Pub Date : 2024-06-18 Wentao Liu, Tong Tian, Lemeng Wang, Weijin Xu, Lei Li, Haoyuan Li, Wenyi Zhao, Siyu Tian, Xipeng Pan, Yiming Deng, Feng Gao, Huihua Yang, Xin Wang, Ruisheng Su
The automated segmentation of Intracranial Arteries (IA) in Digital Subtraction Angiography (DSA) plays a crucial role in the quantification of vascular morphology, significantly contributing to computer-assisted stroke research and clinical practice. Current research primarily focuses on the segmentation of single-frame DSA using proprietary datasets. However, these methods face challenges due to
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CCSI: Continual Class-Specific Impression for data-free class incremental learning Med. Image Anal. (IF 10.7) Pub Date : 2024-06-15 Sana Ayromlou, Teresa Tsang, Purang Abolmaesumi, Xiaoxiao Li
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Anatomically plausible segmentations: Explicitly preserving topology through prior deformations Med. Image Anal. (IF 10.7) Pub Date : 2024-06-15 Madeleine K. Wyburd, Nicola K. Dinsdale, Mark Jenkinson, Ana I.L. Namburete
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Retinal image registration method for myopia development Med. Image Anal. (IF 10.7) Pub Date : 2024-06-15 Zengshuo Wang, Haohan Zou, Yin Guo, Shan Guo, Xin Zhao, Yan Wang, Mingzhu Sun
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Interpretable deep clustering survival machines for Alzheimer’s disease subtype discovery Med. Image Anal. (IF 10.7) Pub Date : 2024-06-14 Bojian Hou, Zixuan Wen, Jingxuan Bao, Richard Zhang, Boning Tong, Shu Yang, Junhao Wen, Yuhan Cui, Jason H. Moore, Andrew J. Saykin, Heng Huang, Paul M. Thompson, Marylyn D. Ritchie, Christos Davatzikos, Li Shen, Alzheimer’s Disease Neuroimaging Initiative
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I[formula omitted]U-Net: A dual-path U-Net with rich information interaction for medical image segmentation Med. Image Anal. (IF 10.7) Pub Date : 2024-06-12 Duwei Dai, Caixia Dong, Qingsen Yan, Yongheng Sun, Chunyan Zhang, Zongfang Li, Songhua Xu
Although the U-shape networks have achieved remarkable performances in many medical image segmentation tasks, they rarely model the sequential relationship of hierarchical layers. This weakness makes it difficult for the current layer to effectively utilize the historical information of the previous layer, leading to unsatisfactory segmentation results for lesions with blurred boundaries and irregular
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Hierarchical online contrastive anomaly detection for fetal arrhythmia diagnosis in ultrasound Med. Image Anal. (IF 10.7) Pub Date : 2024-06-08 Xin Yang, Lian Liu, Zhongnuo Yan, Junxuan Yu, Xindi Hu, Xuejuan Yu, Caixia Dong, Ju Chen, Hongmei Liu, Zhuan Yu, Xuedong Deng, Dong Ni, Xiaoqiong Huang, Zhongshan Gou
Arrhythmia is a major cardiac abnormality in fetuses. Therefore, early diagnosis of arrhythmia is clinically crucial. Pulsed-wave Doppler ultrasound is a commonly used diagnostic tool for fetal arrhythmia. Its key step for diagnosis involves identifying adjacent measurable cardiac cycles (MCCs). As cardiac activity is complex and the experience of sonographers is often varied, automation can improve
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Multi-detector fusion and Bayesian smoothing for tracking viral and chromatin structures Med. Image Anal. (IF 10.7) Pub Date : 2024-06-08 C. Ritter, J.-Y. Lee, M.-T. Pham, M.K. Pabba, M.C. Cardoso, R. Bartenschlager, K. Rohr
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The STOIC2021 COVID-19 AI challenge: Applying reusable training methodologies to private data Med. Image Anal. (IF 10.7) Pub Date : 2024-06-05 Luuk H. Boulogne, Julian Lorenz, Daniel Kienzle, Robin Schön, Katja Ludwig, Rainer Lienhart, Simon Jégou, Guang Li, Cong Chen, Qi Wang, Derik Shi, Mayug Maniparambil, Dominik Müller, Silvan Mertes, Niklas Schröter, Fabio Hellmann, Miriam Elia, Ine Dirks, Matías Nicolás Bossa, Abel Díaz Berenguer, Tanmoy Mukherjee, Jef Vandemeulebroucke, Hichem Sahli, Nikos Deligiannis, Panagiotis Gonidakis, Ngoc Dung
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Universal and extensible language-vision models for organ segmentation and tumor detection from abdominal computed tomography Med. Image Anal. (IF 10.7) Pub Date : 2024-06-04 Jie Liu, Yixiao Zhang, Kang Wang, Mehmet Can Yavuz, Xiaoxi Chen, Yixuan Yuan, Haoliang Li, Yang Yang, Alan Yuille, Yucheng Tang, Zongwei Zhou
The advancement of artificial intelligence (AI) for organ segmentation and tumor detection is propelled by the growing availability of computed tomography (CT) datasets with detailed, per-voxel annotations. However, these AI models often struggle with for partially annotated datasets and for new classes due to limitations in the one-hot encoding, architectural design, and learning scheme. To overcome
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A Bayesian network for simultaneous keyframe and landmark detection in ultrasonic cine Med. Image Anal. (IF 10.7) Pub Date : 2024-06-01 Yong Feng, Jinzhu Yang, Meng Li, Lingzhi Tang, Song Sun, Yonghuai Wang
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Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge Med. Image Anal. (IF 10.7) Pub Date : 2024-05-31 Gregory Holste, Yiliang Zhou, Song Wang, Ajay Jaiswal, Mingquan Lin, Sherry Zhuge, Yuzhe Yang, Dongkyun Kim, Trong-Hieu Nguyen-Mau, Minh-Triet Tran, Jaehyup Jeong, Wongi Park, Jongbin Ryu, Feng Hong, Arsh Verma, Yosuke Yamagishi, Changhyun Kim, Hyeryeong Seo, Myungjoo Kang, Leo Anthony Celi, Zhiyong Lu, Ronald M. Summers, George Shih, Zhangyang Wang, Yifan Peng
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A review of uncertainty quantification in medical image analysis: Probabilistic and non-probabilistic methods Med. Image Anal. (IF 10.7) Pub Date : 2024-05-31 Ling Huang, Su Ruan, Yucheng Xing, Mengling Feng
The comprehensive integration of machine learning healthcare models within clinical practice remains suboptimal, notwithstanding the proliferation of high-performing solutions reported in the literature. A predominant factor hindering widespread adoption pertains to an insufficiency of evidence affirming the reliability of the aforementioned models. Recently, uncertainty quantification methods have
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MMGPL: Multimodal Medical Data Analysis with Graph Prompt Learning Med. Image Anal. (IF 10.7) Pub Date : 2024-05-28 Liang Peng, Songyue Cai, Zongqian Wu, Huifang Shang, Xiaofeng Zhu, Xiaoxiao Li
Prompt learning has demonstrated impressive efficacy in the fine-tuning of multimodal large models to a wide range of downstream tasks. Nonetheless, applying existing prompt learning methods for the diagnosis of neurological disorder still suffers from two issues: (i) existing methods typically treat all patches equally, despite the fact that only a small number of patches in neuroimaging are relevant
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Alzheimer’s disease diagnosis from multi-modal data via feature inductive learning and dual multilevel graph neural network Med. Image Anal. (IF 10.7) Pub Date : 2024-05-28 Baiying Lei, Yafeng Li, Wanyi Fu, Peng Yang, Shaobin Chen, Tianfu Wang, Xiaohua Xiao, Tianye Niu, Yu Fu, Shuqiang Wang, Hongbin Han, Jing Qin, the Alzheimer’s Disease Neuroimaging Initiative
Multi-modal data can provide complementary information of Alzheimer’s disease (AD) and its development from different perspectives. Such information is closely related to the diagnosis, prevention, and treatment of AD, and hence it is necessary and critical to study AD through multi-modal data. Existing learning methods, however, usually ignore the influence of feature heterogeneity and directly fuse
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Comparing regularized Kelvinlet functions and the finite element method for registration of medical images to sparse organ data Med. Image Anal. (IF 10.7) Pub Date : 2024-05-26 Morgan J. Ringel, Jon S. Heiselman, Winona L. Richey, Ingrid M. Meszoely, William R. Jarnagin, Michael I. Miga
Image-guided surgery collocates patient-specific data with the physical environment to facilitate surgical decision making. Unfortunately, these guidance systems commonly become compromised by intraoperative soft-tissue deformations. Nonrigid image-to-physical registration methods have been proposed to compensate for deformations, but clinical utility requires compatibility of these techniques with
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CMAN: Cascaded Multi-scale Spatial Channel Attention-guided Network for large 3D deformable registration of liver CT images Med. Image Anal. (IF 10.7) Pub Date : 2024-05-22 Xuan Loc Pham, Manh Ha Luu, Theo van Walsum, Hong Son Mai, Stefan Klein, Ngoc Ha Le, Duc Trinh Chu
Deformable image registration is an essential component of medical image analysis and plays an irreplaceable role in clinical practice. In recent years, deep learning-based registration methods have demonstrated significant improvements in convenience, robustness and execution time compared to traditional algorithms. However, registering images with large displacements, such as those of the liver organ
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Confidence-aware multi-modality learning for eye disease screening Med. Image Anal. (IF 10.7) Pub Date : 2024-05-22 Ke Zou, Tian Lin, Zongbo Han, Meng Wang, Xuedong Yuan, Haoyu Chen, Changqing Zhang, Xiaojing Shen, Huazhu Fu
Multi-modal ophthalmic image classification plays a key role in diagnosing eye diseases, as it integrates information from different sources to complement their respective performances. However, recent improvements have mainly focused on accuracy, often neglecting the importance of confidence and robustness in predictions for diverse modalities. In this study, we propose a novel multi-modality evidential
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BrainDAS: Structure-aware domain adaptation network for multi-site brain network analysis Med. Image Anal. (IF 10.7) Pub Date : 2024-05-22 Ruoxian Song, Peng Cao, Guangqi Wen, Pengfei Zhao, Ziheng Huang, Xizhe Zhang, Jinzhu Yang, Osmar R. Zaiane
In the medical field, datasets are mostly integrated across sites due to difficult data acquisition and insufficient data at a single site. The domain shift problem caused by the heterogeneous distribution among multi-site data makes autism spectrum disorder (ASD) hard to identify. Recently, domain adaptation has received considerable attention as a promising solution. However, domain adaptation on
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Unsupervised mutual transformer learning for multi-gigapixel Whole Slide Image classification Med. Image Anal. (IF 10.7) Pub Date : 2024-05-21 Sajid Javed, Arif Mahmood, Talha Qaiser, Naoufel Werghi, Nasir Rajpoot
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A self-supervised spatio-temporal attention network for video-based 3D infant pose estimation Med. Image Anal. (IF 10.7) Pub Date : 2024-05-18 Wang Yin, Linxi Chen, Xinrui Huang, Chunling Huang, Zhaohong Wang, Yang Bian, You Wan, Yuan Zhou, Tongyan Han, Ming Yi
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Mask-aware transformer with structure invariant loss for CT translation Med. Image Anal. (IF 10.7) Pub Date : 2024-05-17 Wenting Chen, Wei Zhao, Zhen Chen, Tianming Liu, Li Liu, Jun Liu, Yixuan Yuan
Multi-phase enhanced computed tomography (MPECT) translation from plain CT can help doctors to detect the liver lesion and prevent patients from the allergy during MPECT examination. Existing CT translation methods directly learn an end-to-end mapping from plain CT to MPECT, ignoring the crucial clinical domain knowledge. As clinicians subtract the plain CT from MPECT images as subtraction image to
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TauFlowNet: Revealing latent propagation mechanism of tau aggregates using deep neural transport equations Med. Image Anal. (IF 10.7) Pub Date : 2024-05-17 Tingting Dan, Mustafa Dere, Won Hwa Kim, Minjeong Kim, Guorong Wu
Mounting evidence shows that Alzheimer's disease (AD) is characterized by the propagation of tau aggregates throughout the brain in a prion-like manner. Since current pathology imaging technologies only provide a spatial mapping of tau accumulation, computational modeling becomes indispensable in analyzing the spatiotemporal propagation patterns of widespread tau aggregates from the longitudinal data
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Anonymizing medical case-based explanations through disentanglement Med. Image Anal. (IF 10.7) Pub Date : 2024-05-17 Helena Montenegro, Jaime S. Cardoso
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USFM: A universal ultrasound foundation model generalized to tasks and organs towards label efficient image analysis Med. Image Anal. (IF 10.7) Pub Date : 2024-05-15 Jing Jiao, Jin Zhou, Xiaokang Li, Menghua Xia, Yi Huang, Lihong Huang, Na Wang, Xiaofan Zhang, Shichong Zhou, Yuanyuan Wang, Yi Guo
Inadequate generality across different organs and tasks constrains the application of ultrasound (US) image analysis methods in smart healthcare. Building a universal US foundation model holds the potential to address these issues. Nevertheless, the development of such foundation models encounters intrinsic challenges in US analysis, i.e., insufficient databases, low quality, and ineffective features
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SimCol3D — 3D reconstruction during colonoscopy challenge Med. Image Anal. (IF 10.7) Pub Date : 2024-05-15 Anita Rau, Sophia Bano, Yueming Jin, Pablo Azagra, Javier Morlana, Rawen Kader, Edward Sanderson, Bogdan J. Matuszewski, Jae Young Lee, Dong-Jae Lee, Erez Posner, Netanel Frank, Varshini Elangovan, Sista Raviteja, Zhengwen Li, Jiquan Liu, Seenivasan Lalithkumar, Mobarakol Islam, Hongliang Ren, Laurence B. Lovat, José M.M. Montiel, Danail Stoyanov
Colorectal cancer is one of the most common cancers in the world. While colonoscopy is an effective screening technique, navigating an endoscope through the colon to detect polyps is challenging. A 3D map of the observed surfaces could enhance the identification of unscreened colon tissue and serve as a training platform. However, reconstructing the colon from video footage remains difficult. Learning-based
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MONAI Label: A framework for AI-assisted interactive labeling of 3D medical images Med. Image Anal. (IF 10.7) Pub Date : 2024-05-15 Andres Diaz-Pinto, Sachidanand Alle, Vishwesh Nath, Yucheng Tang, Alvin Ihsani, Muhammad Asad, Fernando Pérez-García, Pritesh Mehta, Wenqi Li, Mona Flores, Holger R. Roth, Tom Vercauteren, Daguang Xu, Prerna Dogra, Sebastien Ourselin, Andrew Feng, M. Jorge Cardoso
The lack of annotated datasets is a major bottleneck for training new task-specific supervised machine learning models, considering that manual annotation is extremely expensive and time-consuming. To address this problem, we present MONAI Label, a free and open-source framework that facilitates the development of applications based on artificial intelligence (AI) models that aim at reducing the time
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Fair evaluation of federated learning algorithms for automated breast density classification: The results of the 2022 ACR-NCI-NVIDIA federated learning challenge Med. Image Anal. (IF 10.7) Pub Date : 2024-05-15 Kendall Schmidt, Benjamin Bearce, Ken Chang, Laura Coombs, Keyvan Farahani, Marawan Elbatel, Kaouther Mouheb, Robert Marti, Ruipeng Zhang, Yao Zhang, Yanfeng Wang, Yaojun Hu, Haochao Ying, Yuyang Xu, Conrad Testagrose, Mutlu Demirer, Vikash Gupta, Ünal Akünal, Markus Bujotzek, Klaus H. Maier-Hein, Yi Qin, Xiaomeng Li, Jayashree Kalpathy-Cramer, Holger R. Roth
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Deep radial basis function networks with subcategorization for mitosis detection in breast histopathology images Med. Image Anal. (IF 10.7) Pub Date : 2024-05-15 Qiling Tang, Yu Cai
Due to the intra-class diversity of mitotic cells and the morphological overlap with similarly looking imposters, automatic mitosis detection in histopathology slides is still a challenging task. In this paper, we propose a novel mitosis detection model in a weakly supervised way, which consists of a candidate proposal network and a verification network. The candidate proposal network based on patch
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Which images to label for few-shot medical image analysis? Med. Image Anal. (IF 10.7) Pub Date : 2024-05-13 Quan Quan, Qingsong Yao, Heqin Zhu, Qiyuan Wang, S. Kevin Zhou
The success of deep learning methodologies hinges upon the availability of meticulously labeled extensive datasets. However, when dealing with medical images, the annotation process for such abundant training data often necessitates the involvement of experienced radiologists, thereby consuming their limited time resources. In order to alleviate this burden, few-shot learning approaches have been developed
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A comprehensive survey on deep active learning in medical image analysis Med. Image Anal. (IF 10.7) Pub Date : 2024-05-13 Haoran Wang, Qiuye Jin, Shiman Li, Siyu Liu, Manning Wang, Zhijian Song
Deep learning has achieved widespread success in medical image analysis, leading to an increasing demand for large-scale expert-annotated medical image datasets. Yet, the high cost of annotating medical images severely hampers the development of deep learning in this field. To reduce annotation costs, active learning aims to select the most informative samples for annotation and train high-performance
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Deep magnetic resonance fingerprinting based on Local and Global Vision Transformer Med. Image Anal. (IF 10.7) Pub Date : 2024-05-13 Peng Li, Yue Hu
To mitigate systematic errors in magnetic resonance fingerprinting (MRF), the precomputed dictionary is usually computed with minimal granularity across the entire range of tissue parameters. However, the dictionary grows exponentially with the number of parameters increase, posing significant challenges to the computational efficiency and matching accuracy of pattern-matching algorithms. Existing
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BRAIxDet: Learning to detect malignant breast lesion with incomplete annotations Med. Image Anal. (IF 10.7) Pub Date : 2024-05-10 Yuanhong Chen, Yuyuan Liu, Chong Wang, Michael Elliott, Chun Fung Kwok, Carlos Peña-Solorzano, Yu Tian, Fengbei Liu, Helen Frazer, Davis J. McCarthy, Gustavo Carneiro
Methods to detect malignant lesions from screening mammograms are usually trained with fully annotated datasets, where images are labelled with the localisation and classification of cancerous lesions. However, real-world screening mammogram datasets commonly have a subset that is fully annotated and another subset that is weakly annotated with just the global classification (i.e., without lesion localisation)
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Labelling with dynamics: A data-efficient learning paradigm for medical image segmentation Med. Image Anal. (IF 10.7) Pub Date : 2024-05-10 Yuanhan Mo, Fangde Liu, Guang Yang, Shuo Wang, Jianqing Zheng, Fuping Wu, Bartłomiej W. Papież, Douglas McIlwraith, Taigang He, Yike Guo
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Classification of lung cancer subtypes on CT images with synthetic pathological priors Med. Image Anal. (IF 10.7) Pub Date : 2024-05-09 Wentao Zhu, Yuan Jin, Gege Ma, Geng Chen, Jan Egger, Shaoting Zhang, Dimitris N. Metaxas
The accurate diagnosis on pathological subtypes for lung cancer is of significant importance for the follow-up treatments and prognosis managements. In this paper, we propose self-generating hybrid feature network (SGHF-Net) for accurately classifying lung cancer subtypes on computed tomography (CT) images. Inspired by studies stating that cross-scale associations exist in the image patterns between
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Involving logical clinical knowledge into deep neural networks to improve bladder tumor segmentation Med. Image Anal. (IF 10.7) Pub Date : 2024-05-08 Xiaodong Yue, Xiao Huang, Zhikang Xu, Yufei Chen, Chuanliang Xu
Segmentation of bladder tumors from medical radiographic images is of great significance for early detection, diagnosis and prognosis evaluation of bladder cancer. Deep Convolution Neural Networks (DCNNs) have been successfully used for bladder tumor segmentation, but the segmentation based on DCNN is data-hungry for model training and ignores clinical knowledge. From the clinical view, bladder tumors
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Longitudinally consistent registration and parcellation of cortical surfaces using semi-supervised learning Med. Image Anal. (IF 10.7) Pub Date : 2024-05-07 Fenqiang Zhao, Zhengwang Wu, Li Wang, Weili Lin, Gang Li
Temporally consistent and accurate registration and parcellation of longitudinal cortical surfaces is of great importance in studying longitudinal morphological and functional changes of human brains. However, most existing methods are developed for registration or parcellation of a single cortical surface. When applying to longitudinal studies, these methods independently register/parcellate each
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TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction Med. Image Anal. (IF 10.7) Pub Date : 2024-05-07 Xueqi Guo, Luyao Shi, Xiongchao Chen, Qiong Liu, Bo Zhou, Huidong Xie, Yi-Hwa Liu, Richard Palyo, Edward J. Miller, Albert J. Sinusas, Lawrence Staib, Bruce Spottiswoode, Chi Liu, Nicha C. Dvornek
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Real-time diagnosis of intracerebral hemorrhage by generating dual-energy CT from single-energy CT Med. Image Anal. (IF 10.7) Pub Date : 2024-05-07 Caiwen Jiang, Tianyu Wang, Yongsheng Pan, Zhongxiang Ding, Dinggang Shen
Real-time diagnosis of intracerebral hemorrhage after thrombectomy is crucial for follow-up treatment. However, this is difficult to achieve with standard single-energy CT (SECT) due to similar CT values of blood and contrast agents under a single energy spectrum. In contrast, dual-energy CT (DECT) scanners employ two different energy spectra, which allows for real-time differentiation between hemorrhage
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Multi-scale relational graph convolutional network for multiple instance learning in histopathology images Med. Image Anal. (IF 10.7) Pub Date : 2024-05-06 Roozbeh Bazargani, Ladan Fazli, Martin Gleave, Larry Goldenberg, Ali Bashashati, Septimiu Salcudean
Graph convolutional neural networks have shown significant potential in natural and histopathology images. However, their use has only been studied in a single magnification or multi-magnification with either homogeneous graphs or only different node types. In order to leverage the multi-magnification information and improve message passing with graph convolutional networks, we handle different embedding
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A systematic comparison of deep learning methods for Gleason grading and scoring Med. Image Anal. (IF 10.7) Pub Date : 2024-05-04 Juan P. Dominguez-Morales, Lourdes Duran-Lopez, Niccolò Marini, Saturnino Vicente-Diaz, Alejandro Linares-Barranco, Manfredo Atzori, Henning Müller
Prostate cancer is the second most frequent cancer in men worldwide after lung cancer. Its diagnosis is based on the identification of the Gleason score that evaluates the abnormality of cells in glands through the analysis of the different Gleason patterns within tissue samples. The recent advancements in computational pathology, a domain aiming at developing algorithms to automatically analyze digitized
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Achieve fairness without demographics for dermatological disease diagnosis Med. Image Anal. (IF 10.7) Pub Date : 2024-05-03 Ching-Hao Chiu, Yu-Jen Chen, Yawen Wu, Yiyu Shi, Tsung-Yi Ho
In medical image diagnosis, fairness has become increasingly crucial. Without bias mitigation, deploying unfair AI would harm the interests of the underprivileged population and potentially tear society apart. Recent research addresses prediction biases in deep learning models concerning demographic groups (e.g., gender, age, and race) by utilizing demographic (sensitive attribute) information during
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Self-supervised anatomical continuity enhancement network for 7T SWI synthesis from 3T SWI Med. Image Anal. (IF 10.7) Pub Date : 2024-05-03 Dong Zhang, Caohui Duan, Udunna Anazodo, Z. Jane Wang, Xin Lou
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Standardization of ultrasound images across various centers: M2O-DiffGAN bridging the gaps among unpaired multi-domain ultrasound images Med. Image Anal. (IF 10.7) Pub Date : 2024-04-25 Lihong Huang, Jin Zhou, Jing Jiao, Shichong Zhou, Cai Chang, Yuanyuan Wang, Yi Guo
Domain shift problem is commonplace for ultrasound image analysis due to difference imaging setting and diverse medical centers, which lead to poor generalizability of deep learning-based methods. Multi-Source Domain Transformation (MSDT) provides a promising way to tackle the performance degeneration caused by the domain shift, which is more practical and challenging compared to conventional single-source
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Deep learning microstructure estimation of developing brains from diffusion MRI: A newborn and fetal study Med. Image Anal. (IF 10.7) Pub Date : 2024-04-25 Hamza Kebiri, Ali Gholipour, Rizhong Lin, Lana Vasung, Camilo Calixto, Željka Krsnik, Davood Karimi, Meritxell Bach Cuadra
Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. Fiber orientation distribution functions (FODs) are a common way of representing the orientation and density of white matter fibers. However, with standard FOD computation methods, accurate estimation requires a large number of measurements that usually cannot be acquired for newborns and fetuses.
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One-shot neuroanatomy segmentation through online data augmentation and confidence aware pseudo label Med. Image Anal. (IF 10.7) Pub Date : 2024-04-25 Liutong Zhang, Guochen Ning, Hanying Liang, Boxuan Han, Hongen Liao