Using deep learning models to diagnose cancer from histology data presents several challenges. Cancer grading and localization of regions of interest (ROIs) in these images normally relies on both image- and pixel-level labels, the latter requiring a costly annotation process. Deep weakly-supervised object localization (WSOL) methods provide different strategies for low-cost training of deep learning models. Using only image-class annotations, these methods can be trained to classify an image, and yield class activation maps (CAMs) for ROI localization. This paper provides a review of state-of-art DL methods for WSOL. We propose a taxonomy where these methods are divided into bottom-up and top-down methods according to the information flow in models. Although the latter have seen limited progress, recent bottom-up methods are currently driving much progress with deep WSOL methods. Early works focused on designing different spatial pooling functions. However, these methods reached limited localization accuracy, and unveiled a major limitation -- the under-activation of CAMs which leads to high false negative localization. Subsequent works aimed to alleviate this issue and recover complete object. Representative methods from our taxonomy are evaluated and compared in terms of classification and localization accuracy on two challenging histology datasets. Overall, the results indicate poor localization performance, particularly for generic methods that were initially designed to process natural images. Methods designed to address the challenges of histology data yielded good results. However, all methods suffer from high false positive/negative localization. Four key challenges are identified for the application of deep WSOL methods in histology -- under/over activation of CAMs, sensitivity to thresholding, and model selection.

翻译：利用深度学习模型从组织学数据中诊断癌症面临着多重挑战。癌症分级和感兴趣区域（ROIs）的定位通常依赖于图像级和像素级标签，而后者的标注过程成本高昂。深度弱监督目标定位（WSOL）方法为深度学习模型的低成本训练提供了多种策略。仅使用图像类别标注，这些方法即可训练分类模型，并生成用于ROI定位的类激活映射（CAMs）。本文综述了当前最先进的深度学习WSOL方法。我们提出了一种分类体系，根据模型中的信息流将这些方法分为自底向上和自顶向下两类。尽管自顶向下方法进展有限，但近期自底向上方法正推动着基于深度WSOL方法的重大进步。早期工作聚焦于设计不同的空间池化函数，但这些方法的定位精度有限，并揭示了一个主要局限性——CAMs的低激活导致高假阴性定位。后续研究旨在缓解该问题并恢复完整目标。我们分类体系中的代表性方法在两个具有挑战性的组织学数据集上进行了分类与定位精度的评估比较。总体而言，结果表明定位性能较差，尤其是最初设计用于处理自然图像的通用方法。针对组织学数据挑战而专门设计的方法取得了良好效果，但所有方法均存在高假阳性/假阴性定位问题。本文确定了深度WSOL方法在组织学应用中的四个关键挑战：CAMs的低激活/过激活、对阈值设定的敏感性以及模型选择。