Semi-supervised learning for cancer detection of lymph node metastases
AffiliationUniversity of Bedfordshire
Perm State University
Universiti Brunei Darussalam
MetadataShow full item record
AbstractPathologists find tedious to examine the status of the sentinel lymph node on a large number of pathological scans. The examination process of such lymph node which encompasses metastasized cancer cells is histopathologically organized. However, the task of finding metastatic tissues is gradual which is often challenging. In this work, we present our deep convolutional neural network based model validated on PatchCamelyon (PCam) benchmark dataset for fundamental machine learning research in histopathology diagnosis. We find that our proposed model trained with a semi-supervised learning approach by using pseudo labels on PCam-level significantly leads to better performances to strong CNN baseline on the AUC metric.
CitationJaiswal AK, Panshin I, Shulkin D, Aneja N, Abramov S (2019) 'Semi-supervised learning for cancer detection of lymph node metastases', CVPR - Towards Causal, Explainable and Universal Medical Visual Diagnosis - Long Beach, .
TypeConference papers, meetings and proceedings
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- Creative Commons
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc-nd/4.0/