Description
Title:
Multiple instance learning in (label-free) computational pathology
Abstract:
Computational pathology is mainly concerned with the medical image analysis of patient specimens for the study of disease. The development of novel decision support systems is one way to further personalized medicine efforts and enable differential diagnosis systems for early detection and characterization of diseases such as colorectal or urothelial carcinoma. With recent advances in
(label-free) digital pathology, conventional glass slides carrying patient specimens can now be digitized into gigapixel whole slide images. Analyzing those images is mostly facilitated by deep learning-based artificial intelligence approaches. Here, a key challenge is the lack of large-scale annotations which serve as the ground truth to learn from. While many methods were based on strongly supervised learning with relatively small amounts of intricately labeled data, there is now a shift towards much larger datasets with weak labeling due to cheaper storage and computational resources, but also because of the large efforts required to obtain professional annotations.
Novel deep learning approaches from the realm of multiple instance learning are now used to characterize cancer in cells and tissues from a spectral and morphological perspective. Infrared microscopy provides unstained, spatially-resolved spectral images, with biochemical fingerprints at each pixel in an image, enabling the discovery of underlying changes in genome, proteome or metabolome. But also light microscopic morphological information is analyzed, comparable to standard clinical pathology practice, considering form and structure of cells and tissues via staining dyes. By either learning robust representations of high-level features or finding key elements within the data instances automatically and without additional annotation efforts, whole slide images can be analyzed effectively. This domain-adaptable characterization can aid clinical diagnosis and therapy recommendations by providing a new, non invasive alternative of urine cytology based on morphological changes that differentiate cancerous urothelial cells from non-cancerous ones. Similarly, spectral changes in colorectal tissue can reveal the microsatellite status, an important indicator that recommends patients for specialized immunotherapy, without needing any laborious and not widely employed staining procedures.
