CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide Images

The visual examination of tissue biopsy sections is fundamental for cancer diagnosis, with pathologists analyzing sections at multiple magnifications to discern tumor cells and their subtypes. However, existing attention-based multiple instance learning (MIL) models, used for analyzing Whole Slide Images (WSIs) in cancer diagnostics, often overlook the contextual information of tumor and neighboring tiles, leading to misclassifications. To address this, we propose the Context-Aware Multiple Instance Learning (CAMIL) architecture. CAMIL incorporates neighbor-constrained attention to consider dependencies among tiles within a WSI and integrates contextual constraints as prior knowledge into the MIL model. We evaluated CAMIL on subtyping non-small cell lung cancer (TCGA-NSCLC) and detecting lymph node (CAMELYON16) metastasis, achieving test AUCs of 0.959\% and 0.975\%, respectively, outperforming other state-of-the-art methods. Additionally, CAMIL enhances model interpretability by identifying regions of high diagnostic value.Comment: 16 pages, 4 figure

Using unaltered, variable sized and high-resolution images for training fully-convolutional networks

Motivated by the usefulness of high resolution images in a broad range of applications, such as medical imaging, astronomy and video surveillance, in this thesis we investigate training a convolutions neural network with unaltered high-resolution images

Using unaltered, variable sized and high-resolution images for training fully-convolutional networks

Motivated by the usefulness of high resolution images in a broad range of applications, such as medical imaging, astronomy and video surveillance, in this thesis we investigate training a convolutions neural network with unaltered high-resolution images