HistoLung project as part of the ExaMode project

As part of my internship I was told to develop a model to be able to classify different cancer subtypes from digital pathology images. The results were accepted and presented at the SPIE Medical Imaging 2024 conference in San Diego. GitHub to histo_lung

The figure above presents the Full Pipeline to train the lung cancer subtype classification model. A: Preprocessing of the Whole Slide Images (WSI) for patch extraction. B: Self-supervised learning to pre-train the feature extractor capturing high-level concepts directly from the histopathological lung patches using Momentum Contrastive Learning (MoCo v2). C: Training of a Multiple Instance Learning (MIL) model for the lung cancer classification task among, LUng ADenocarcinoma (LUAD), LUng Squamous cell Carcinoma (LUSC), Small-Cell Lung Cancer (SCLC), and NormaL tissue (NL), using a multi-label strategy.
For qualitative evaluation of the MIL model performance, a heatmap is computed on the model with the best performance. The following figure shows that the model obtains a higher attention score in two of the three areas where the LUSC cancer is located as pointed out by the manual annotations from an expert pathologist.

The Uniform Manifold Approximation and Projection (UMAP) is computed from the features extracted from the self-supervised model on 384 patches (135 cells, 158 glands, and 91 stroma) selected by an expert pathologist. Qualitatively, as shown in the next image the self-supervised model can almost perfectly cluster the three types of patches into clear separable regions.