ArticlePattern recognition2018
Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks.
Article in Pattern recognition, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
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Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review on deep learning-based automated cancer diagnosis models.Journal of cellular and molecular medicine · 2024Pooled it
- Improving Ovarian Cancer Subtyping with Computer Vision Models on Tiled Histopathological Images.Journal of imaging informatics in medicine · 2026Article
- Automatic identification of clinically importantEmerging microbes & infections · 2025Article
- Deep learning predicts the effect of neoadjuvant chemotherapy for patients with triple negative breast cancer.Journal of pathology informatics · 2025Article
- Automatic Blob Detection Method for Cancerous Lesions in Unsupervised Breast Histology Images.Bioengineering (Basel, Switzerland) · 2025Article
- Current status and prospects of artificial intelligence in breast cancer pathology: convolutional neural networks to prospective Vision Transformers.International journal of clinical oncology · 2024Review
- A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging.Journal of imaging · 2024Article
- Oral epithelial dysplasia detection and grading in oral leukoplakia using deep learning.BMC oral health · 2024Article
- Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies.Journal of cancer research and clinical oncology · 2023Review
- Artificial Intelligence in Histologic Diagnosis of Ductal Carcinoma In Situ.Mayo Clinic proceedings. Digital health · 2023Review
- Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology.Cancer reports (Hoboken, N.J.) · 2023Review
- Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation.Journal of pathology informatics · 2023Article
- Application of Deep Learning in Histopathology Images of Breast Cancer: A Review.Micromachines · 2022Review
- Covid-MANet: Multi-task attention network for explainable diagnosis and severity assessment of COVID-19 from CXR images.Pattern recognition · 2022Article
- Artificial intelligence in histopathology: enhancing cancer research and clinical oncology.Nature cancer · 2022Review
- End-to-End diagnosis of breast biopsy images with transformers.Medical image analysis · 2022Article
- REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays.Scientific data · 2022Article
- A Novel Method Based on GAN Using a Segmentation Module for Oligodendroglioma Pathological Image Generation.Sensors (Basel, Switzerland) · 2022Article
- Fast Segmentation of Metastatic Foci in H&E Whole-Slide Images for Breast Cancer Diagnosis.Diagnostics (Basel, Switzerland) · 2022Article
- Progress on deep learning in digital pathology of breast cancer: a narrative review.Gland surgery · 2022Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Generalizability of algorithms for binary cancer vs. no cancer classification is unknown for clinically more significant multi-class scenarios where intermediate categories have different risk factors and treatment strategies. We present a system that classifies whole slide images (WSI) of breast biopsies into five diagnostic categories. First, a saliency detector that uses a pipeline of four fully convolutional networks, trained with samples from records of pathologists' screenings, performs multi-scale localization of diagnostically relevant regions of interest in WSI. Then, a convolutional network, trained from consensus-derived reference samples, classifies image patches as non-proliferative or proliferative changes, atypical ductal hyperplasia, ductal carcinoma in situ, and invasive carcinoma. Finally, the saliency and classification maps are fused for pixel-wise labeling and slide-level categorization. Experiments using 240 WSI showed that both saliency detector and classifier networks performed better than competing algorithms, and the five-class slide-level accuracy of 55% was not statistically different from the predictions of 45 pathologists. We also present example visualizations of the learned representations for breast cancer diagnosis.
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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.