Segmentation is a critical task in computational pathology, as it identifies areas affected by disease or abnormal growth and is essential for diagnosis and treatment. However, acquiring high-quality pixel-level supervised segmentation data requires significant workload demands from experienced pathologists, limiting the application of deep learning. To overcome this challenge, relaxing the label conditions to image-level classification labels allows for more data to be used and more scenarios to be enabled. One approach is to leverage Class Activation Map (CAM) to generate pseudo pixel-level annotations for semantic segmentation with only image-level labels. However, this method fails to thoroughly explore the essential characteristics of pathology images, thus identifying only small areas that are insufficient for pseudo masking. In this paper, we propose a novel shuffle-based feedback learning method inspired by curriculum learning to generate higher-quality pseudo-semantic segmentation masks. Specifically, we perform patch level shuffle of pathology images, with the model adaptively adjusting the shuffle strategy based on feedback from previous learning. Experimental results demonstrate that our proposed approach outperforms state-of-the-arts on three different datasets.

翻译：分割是计算病理学中的关键任务，因为它能识别受疾病或异常生长影响的区域，对诊断和治疗至关重要。然而，获取高质量的像素级监督分割数据需要经验丰富的病理学家投入大量工作，这限制了深度学习的应用。为应对这一挑战，将标注条件放宽至图像级分类标签可允许使用更多数据并适用于更多场景。一种方法是利用类激活映射（CAM）生成仅基于图像级标签的伪像素级标注用于语义分割。然而，该方法未能充分探索病理图像的本质特征，因此只能识别出不足以生成伪掩膜的小区域。本文受课程学习启发，提出一种新颖的基于混洗的反馈学习方法，用于生成更高质量的伪语义分割掩膜。具体而言，我们对病理图像进行块级混洗，模型根据先前学习的反馈自适应调整混洗策略。实验结果表明，我们提出的方法在三个不同数据集上均优于现有最佳方法。