Learning with less data via Weakly Labeled Patch Classification in Digital Pathology
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Abstract (arXiv)
In Digital Pathology (DP), labeled data is generally very scarce due to the requirement that medical experts provide annotations. We address this issue by learning transferable features from weakly labeled data, which are collected from various parts of the body and are organized by non-medical experts. In this paper, we show that features learned from such weakly labeled datasets are indeed transferable and allow us to achieve highly competitive patch classification results on the colorectal cancer (CRC) dataset [1] and the PatchCamelyon (PCam) dataset [2] while using an order of magnitude less labeled data.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Nov 2019.
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Citing papers per year: 2021: 1; 2022: 5; 2023: 1; 2024: 1.
8 papers.
- 2022Towards better understanding and better generalization of few-shot classification in histology images with contrastive learningcited by 16
- 2023Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directionscited by 11
- 2022Spectral, Probabilistic, and Deep Metric Learning: Tutorial and Surveycited by 10
- 2021Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classificationcited by 1
- 2022Learning with Less Labels in Digital Pathology via Scribble Supervision from Natural Imagescited by 0
- 2022Understanding the impact of image and input resolution on deep digital pathology patch classifierscited by 0
- 2022Attention-based Dynamic Subspace Learners for Medical Image Analysiscited by 0
- 2024Using Few-Shot Learning to Classify Primary Lung Cancer and Other Malignancy with Lung Metastasis in Cytological Imaging via Endobronchial Ultrasound Procedurescited by 0
- 2024Using Few-Shot Learning to Classify Primary Lung Cancer and Other Malignancy with Lung Metastasis in Cytological Imaging via Endobronchial Ultrasound Procedures9 Apr 2024
- 2023Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions13 Apr 2023
- 2022Attention-based Dynamic Subspace Learners for Medical Image Analysis18 Jun 2022
- 2022Understanding the impact of image and input resolution on deep digital pathology patch classifiers29 Apr 2022
- 2022Towards better understanding and better generalization of few-shot classification in histology images with contrastive learning18 Feb 2022
- 2022Spectral, Probabilistic, and Deep Metric Learning: Tutorial and Survey23 Jan 2022
- 2022Learning with Less Labels in Digital Pathology via Scribble Supervision from Natural Images7 Jan 2022
- 2021Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classification13 Jul 2021