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• W2950240113 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." @default.
• W2950240113 created "2019-06-27" @default.
• W2950240113 creator A5048304735 @default.
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• W2950240113 date "2019-06-14" @default.
• W2950240113 modified "2023-09-27" @default.
• W2950240113 title "Semi-Supervised Learning for Cancer Detection of Lymph Node Metastases" @default.
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• W2950240113 doi "https://doi.org/10.5281/zenodo.3251086" @default.
• W2950240113 hasPublicationYear "2019" @default.
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