The computer-aided analysis of medical scans is a longstanding goal in the medical imaging field. Currently, deep learning has became a dominant methodology for supporting pathologists and radiologist. Deep learning algorithm have been successfully applied to digital pathology and radiology, nevertheless, there are still practical issues that prevent these tool to be widely used in practice. The main obstacles are low number of available cases and large size of images (a.k.a. the small n, large p problem in machine learning), and a very limited access to annotation at a pixel level that can lead to severe overfitting and large computational requirements. We propose to handle these issues by introducing a framework that processes a medical image as a collection of small patches using a single, shared neural network. The final diagnosis is provided by combining scores of individual patches using a permutation-invariant operator (combination). In machine learning community such approach is called a multi-instance learning (MIL).

该论文提出了一种处理医学影像的框架，使用多实例学习多个小图像块的神经网络得出最终诊断，以解决机器学习中小n，大p问题和像素级注释的限制。

使用置换不变算子的深度学习进行多实例组织病理分类