组织病理学图像的高效分类
Efficient Classification of Histopathology Images
AI总结：
本研究提出一种分治策略解决组织病理学图像块级类别不平衡问题，通过数据划分、信息论聚类采样构建子模型并整合决策，在仅使用极低比例图像块时仍具竞争力。
摘要：
这项工作解决了如何高效分类具有挑战性的组织病理学图像，例如用于癌症诊断的千兆像素全切片图像（仅带图像级标注）。我们使用带有肿瘤区域标注的图像，在癌变切片中识别出一组肿瘤图像块和一组良性图像块。由于感兴趣区域的可变性，肿瘤阳性区域可能仅占极少数像素。这在图像块级分类中引发了一个重要问题：来自标记为“癌变”图像的多数图像块实际上是无肿瘤的。该问题不同于为图像中每个像素分配标签的语义分割，因为在提取图像块后，我们仅处理图像块级标签。现有方法大多通过缓解少数类数据不足来解决数据不平衡问题，以防止模型被多数类主导。这些方法包括数据重采样、损失重加权、间隔修改和数据增强。在本工作中，我们采用分治策略来缓解图像块级类别不平衡问题。首先，我们将数据划分为子组，并基于这些数据划分定义三个独立的分类子问题。然后，利用基于深度图像块特征的信息论聚类采样，从子组中采样具有区分性的图像块。使用这些采样的图像块，我们构建相应的深度模型来解决新的分类子问题。最后，我们整合从各个模型学习到的信息，对图像块做出最终决策。结果表明，所提出的方法在给定全切片图像中仅使用极低百分比的可用图像块即可实现具有竞争力的性能。
英文摘要：
This work addresses how to efficiently classify challenging histopathology images, such as gigapixel whole-slide images for cancer diagnostics with image-level annotation. We use images with annotated tumor regions to identify a set of tumor patches and a set of benign patches in a cancerous slide. Due to the variable nature of region of interest the tumor positive regions may refer to an extreme minority of the pixels. This creates an important problem during patch-level classification, where the majority of patches from an image labeled as 'cancerous' are actually tumor-free. This problem is different from semantic segmentation which associates a label to every pixel in an image, because after patch extraction we are only dealing with patch-level labels.Most existing approaches address the data imbalance issue by mitigating the data shortage in minority classes in order to prevent the model from being dominated by the majority classes. These methods include data re-sampling, loss re-weighting, margin modification, and data augmentation. In this work, we mitigate the patch-level class imbalance problem by taking a divide-and-conquer approach. First, we partition the data into sub-groups, and define three separate classification sub-problems based on these data partitions. Then, using an information-theoretic cluster-based sampling of deep image patch features, we sample discriminative patches from the sub-groups. Using these sampled patches, we build corresponding deep models to solve the new classification sub-problems. Finally, we integrate information learned from the respective models to make a final decision on the patches. Our result shows that the proposed approach can perform competitively using a very low percentage of the available patches in a given whole-slide image.
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