Jaiswal, AK orcid.org/0000-0001-8848-7041, Panshin, I, Shulkin, D et al. (2 more authors) (2019) Semi-Supervised Learning for Cancer Detection of Lymph Node Metastases. [Preprint - arXiv]
Abstract
Pathologists 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.
Metadata
Item Type: | Preprint |
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Authors/Creators: |
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Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 22 Nov 2024 11:27 |
Last Modified: | 22 Nov 2024 11:27 |
Identification Number: | 10.48550/arXiv.1906.09587 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:173381 |