A Data-Efficient Deep Learning Framework for Segmentation and Classification of Histopathology Images
This work addresses the need for more efficient analysis of histopathology images in autoimmune disease research, though it is incremental as it builds on existing deep learning methods.
The paper tackled the problem of understanding cell architecture in histopathology images for autoimmune diseases by developing deep learning approaches for segmentation and classification, resulting in a 26% improvement in classification performance and a 5% improvement in segmentation performance, with an additional 3% gain from a novel post-processing autoencoder.
The current study of cell architecture of inflammation in histopathology images commonly performed for diagnosis and research purposes excludes a lot of information available on the biopsy slide. In autoimmune diseases, major outstanding research questions remain regarding which cell types participate in inflammation at the tissue level, and how they interact with each other. While these questions can be partially answered using traditional methods, artificial intelligence approaches for segmentation and classification provide a much more efficient method to understand the architecture of inflammation in autoimmune disease, holding great promise for novel insights. In this paper, we empirically develop deep learning approaches that use dermatomyositis biopsies of human tissue to detect and identify inflammatory cells. Our approach improves classification performance by 26% and segmentation performance by 5%. We also propose a novel post-processing autoencoder architecture that improves segmentation performance by an additional 3%.