ArticleScientific reports2022
A loss-based patch label denoising method for improving whole-slide image analysis using a convolutional neural network.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Article
- Leveraging commonality across multiple tissue slices for enhanced whole slide image classification using graph convolutional networks.BMC medical imaging · 2025Article
- Mitigating bias in prostate cancer diagnosis using synthetic data for improved AI driven Gleason grading.NPJ precision oncology · 2025Article
- MPVT+: a noise-robust training framework for automatic liver tumor segmentation with noisy labels.Frontiers in medicine · 2025Article
- Deep learning with noisy labels in medical prediction problems: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- LensePro: label noise-tolerant prototype-based network for improving cancer detection in prostate ultrasound with limited annotations.International journal of computer assisted radiology and surgery · 2024Article
- Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy.NPJ digital medicine · 2024Review
- A predicted-loss based active learning approach for robust cancer pathology image analysis in the workplace.BMC medical imaging · 2024Article
- Using multi-label ensemble CNN classifiers to mitigate labelling inconsistencies in patch-level Gleason grading.PloS one · 2024Article
- Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance.Diagnostics (Basel, Switzerland) · 2023Review
- Deep learning-enabled virtual histological staining of biological samples.Light, science & applications · 2023Review
- Improving Representation Learning for Histopathologic Images with Cluster Constraints.Proceedings. IEEE International Conference on Computer Vision · 2023Article
- Article
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5 authors.
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Abstract
This paper proposes a deep learning-based patch label denoising method (LossDiff) for improving the classification of whole-slide images of cancer using a convolutional neural network (CNN). Automated whole-slide image classification is often challenging, requiring a large amount of labeled data. Pathologists annotate the region of interest by marking malignant areas, which pose a high risk of introducing patch-based label noise by involving benign regions that are typically small in size within the malignant annotations, resulting in low classification accuracy with many Type-II errors. To overcome this critical problem, this paper presents a simple yet effective method for noisy patch classification. The proposed method, validated using stomach cancer images, provides a significant improvement compared to other existing methods in patch-based cancer classification, with accuracies of 98.81%, 97.30% and 89.47% for binary, ternary, and quaternary classes, respectively. Moreover, we conduct several experiments at different noise levels using a publicly available dataset to further demonstrate the robustness of the proposed method. Given the high cost of producing explicit annotations for whole-slide images and the unavoidable error-prone nature of the human annotation of medical images, the proposed method has practical implications for whole-slide image annotation and automated cancer diagnosis.
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