2019/11/27 by Eu Wern Teh, Graham W. Taylor, Teh, Eu Wern +1
Computer Science · #AI in cancer detection #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Image Retrieval and Classification Techniques
paper · pdf · doi:10.48550/arxiv.1911.12425
openalex publication_date 2019/11/27 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
In Digital Pathology (DP), labeled data is generally very scarce due to the\nrequirement that medical experts provide annotations. We address this issue by\nlearning transferable features from weakly labeled data, which are collected\nfrom various parts of the body and are organized by non-medical experts. In\nthis paper, we show that features learned from such weakly labeled datasets are\nindeed transferable and allow us to achieve highly competitive patch\nclassification results on the colorectal cancer (CRC) dataset [1] and the\nPatchCamelyon (PCam) dataset [2] while using an order of magnitude less labeled\ndata.\n