Colorectal cancer plays a dominant role in cancer-related deaths, primarily due to the absence of obvious early-stage symptoms. Whole-stage colorectal disease diagnosis is crucial for assessing lesion evolution and determining treatment plans. However, locality difference and disease progression lead to intra-class disparities and inter-class similarities for colorectal lesion representation. In addition, interpretable algorithms explaining the lesion progression are still lacking, making the prediction process a "black box". In this paper, we propose IPNet, a dual-branch interpretable network with progressive loss for whole-stage colorectal disease diagnosis. The dual-branch architecture captures unbiased features representing diverse localities to suppress intra-class variation. The progressive loss function considers inter-class relationship, using prior knowledge of disease evolution to guide classification. Furthermore, a novel Grain-CAM is designed to interpret IPNet by visualizing pixel-wise attention maps from shallow to deep layers, providing regions semantically related to IPNet's progressive classification. We conducted whole-stage diagnosis on two image modalities, i.e., colorectal lesion classification on 129,893 endoscopic optical images and rectal tumor T-staging on 11,072 endoscopic ultrasound images. IPNet is shown to surpass other state-of-the-art algorithms, accordingly achieving an accuracy of 93.15% and 89.62%. Especially, it establishes effective decision boundaries for challenges like polyp vs. adenoma and T2 vs. T3. The results demonstrate an explainable attempt for colorectal lesion classification at a whole-stage level, and rectal tumor T-staging by endoscopic ultrasound is also unprecedentedly explored. IPNet is expected to be further applied, assisting physicians in whole-stage disease diagnosis and enhancing diagnostic interpretability.

中文翻译：

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IPNet：用于全阶段结直肠疾病诊断的渐进式损失的可解释网络


结直肠癌在癌症相关死亡中占主导地位，这主要是由于没有明显的早期症状。全阶段结直肠疾病诊断对于评估病变演变和确定治疗方案至关重要。然而，局部差异和疾病进展导致结直肠病变表征的类内差异和类间相似性。此外，仍然缺乏解释病变进展的可解释算法，使得预测过程成为“黑匣子”。在本文中，我们提出了 IPNet，一种具有渐进损失的双分支可解释网络，用于全阶段结直肠疾病诊断。双分支架构捕获代表不同位置的无偏特征，以抑制类内变异。渐进损失函数考虑类间关系，利用疾病进化的先验知识来指导分类。此外，一种新颖的 Grain-CAM 旨在通过可视化从浅层到深层的像素级注意力图来解释 IPNet，提供与 IPNet 渐进分类语义相关的区域。我们对两种图像模式进行了全阶段诊断，即对129,893张内镜光学图像进行结直肠病变分类，对11,072张内镜超声图像进行直肠肿瘤T分期。 IPNet 被证明超越了其他最先进的算法，相应地达到了 93.15% 和 89.62% 的准确率。特别是，它为息肉与腺瘤以及 T2 与 T3 等挑战建立了有效的决策边界。结果表明，在全阶段水平上对结直肠病变分类进行了可解释的尝试，并且通过内镜超声对直肠肿瘤T分期也进行了前所未有的探索。 IPNet有望得到进一步应用，辅助医生进行全阶段疾病诊断，增强诊断的可解释性。