ArticleProceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification.
Article in Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 239 papers, 1 of them a synthesis that pooled it.
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Who cites it
239 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association.The Journal of pathology · 2019Guideline
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- Detection of benign prostatic hyperplasia using RGB prostate images and deep learning.Scientific reports · 2026Article
- Inverse-designed nanophotonic neural network accelerators for ultra-compact optical computing.Nature communications · 2026Article
- BiChemoCLAM: a weakly supervised multimodal framework for chemotherapy response prediction.Briefings in bioinformatics · 2026Article
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
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- Construction and exploratory validation of a digital pathology prognostic model for stage II-III colorectal cancer based on contrastive learning and attention mechanisms.Frontiers in oncology · 2026Article
- Harnessing interpretable deep learning to predict resistance inFrontiers in cellular and infection microbiology · 2026Article
- Artificial intelligence-driven prediction of lymph node metastasis in T1 esophageal squamous cell carcinoma using whole slide images.NPJ precision oncology · 2025Article
- A Robust Framework for Domain-Generalized Classification of Ovarian Cancer Histology Images.Diagnostics (Basel, Switzerland) · 2025Article
- Self-supervision enhances instance-based multiple instance learning methods in digital pathology: a benchmark study.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Review
- CLASS-M: Adaptive stain separation-based contrastive learning with pseudo-labeling for histopathological image classification.Medical image analysis · 2025Article
- Learnable Context in Multiple Instance Learning for Whole Slide Image Classification and Segmentation.Journal of imaging informatics in medicine · 2025Article
- Transformer optimization with meta learning on pathology images for breast cancer lymph node micrometastasis.NPJ digital medicine · 2025Article
- OKEN: A Supervised Evolutionary Optimizable Dimensionality Reduction Framework for Whole Slide Image Classification.Bioengineering (Basel, Switzerland) · 2025Article
- AI-assisted Diagnosis of Nonmelanoma Skin Cancer in Resource-Limited Settings.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025Article
179 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Convolutional Neural Networks (CNN) are state-of-the-art models for many image classification tasks. However, to recognize cancer subtypes automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images (WSI) is currently computationally impossible. The differentiation of cancer subtypes is based on cellular-level visual features observed on image patch scale. Therefore, we argue that in this situation, training a patch-level classifier on image patches will perform better than or similar to an image-level classifier. The challenge becomes how to intelligently combine patch-level classification results and model the fact that not all patches will be discriminative. We propose to train a decision fusion model to aggregate patch-level predictions given by patch-level CNNs, which to the best of our knowledge has not been shown before. Furthermore, we formulate a novel Expectation-Maximization (EM) based method that automatically locates discriminative patches robustly by utilizing the spatial relationships of patches. We apply our method to the classification of glioma and non-small-cell lung carcinoma cases into subtypes. The classification accuracy of our method is similar to the inter-observer agreement between pathologists. Although it is impossible to train CNNs on WSIs, we experimentally demonstrate using a comparable non-cancer dataset of smaller images that a patch-based CNN can outperform an image-based CNN.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.