Stain based contrastive co-training for histopathological image analysis

• URL: http://arxiv.org/abs/2206.12505v1
• Date: Fri, 24 Jun 2022 22:25:31 GMT
• Title: Stain based contrastive co-training for histopathological image analysis
• Authors: Bodong Zhang, Beatrice Knudsen, Deepika Sirohi, Alessandro Ferrero, Tolga Tasdizen
• Abstract summary: We propose a novel semi-supervised learning approach for classification of histovolution images. We employ strong supervision with patch-level annotations combined with a novel co-training loss to create a semi-supervised learning framework. We evaluate our approach in clear cell renal cell and prostate carcinomas, and demonstrate improvement over state-of-the-art semi-supervised learning methods.
• Score: 61.87751502143719
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel semi-supervised learning approach for classification of histopathology images. We employ strong supervision with patch-level annotations combined with a novel co-training loss to create a semi-supervised learning framework. Co-training relies on multiple conditionally independent and sufficient views of the data. We separate the hematoxylin and eosin channels in pathology images using color deconvolution to create two views of each slide that can partially fulfill these requirements. Two separate CNNs are used to embed the two views into a joint feature space. We use a contrastive loss between the views in this feature space to implement co-training. We evaluate our approach in clear cell renal cell and prostate carcinomas, and demonstrate improvement over state-of-the-art semi-supervised learning methods.

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