ArticleScientific reports2019
A Fast and Refined Cancer Regions Segmentation Framework in Whole-slide Breast Pathological Images.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Hierarchical vision transformers for Epstein-Barr virus status and histological subtype prediction in Hodgkin lymphoma whole-slide images.Journal of pathology informatics · 2026Article
- Deep-learning-based breast cancer stage prediction from H&E-stained whole-slide images in resource-constrained settings.Journal of pathology informatics · 2026Article
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- Breast cancer detection based on histological images using fusion of diffusion model outputs.Scientific reports · 2025Article
- Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review.Journal of pathology informatics · 2024Article
- A deep learning model to predict Ki-67 positivity in oral squamous cell carcinoma.Journal of pathology informatics · 2024Article
- SVPath: A Deep Learning Tool for Analysis of Stria Vascularis from Histology Slides.Journal of the Association for Research in Otolaryngology : JARO · 2024Article
- Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance.Diagnostics (Basel, Switzerland) · 2023Review
- Grading of lung adenocarcinomas with simultaneous segmentation by artificial intelligence (GLASS-AI).NPJ precision oncology · 2023Article
- Application of Artificial Intelligence in Pathology: Trends and Challenges.Diagnostics (Basel, Switzerland) · 2022Review
- The Role of Deep Learning in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review.Cancers · 2022Review
- Using Sparse Patch Annotation for Tumor Segmentation in Histopathological Images.Sensors (Basel, Switzerland) · 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
- Fast whole-slide cartography in colon cancer histology using superpixels and CNN classification.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Computer-assisted mitotic count using a deep learning-based algorithm improves interobserver reproducibility and accuracy.Veterinary pathology · 2022Article
- A promising deep learning-assistive algorithm for histopathological screening of colorectal cancer.Scientific reports · 2022Article
- BrainSec: Automated Brain Tissue Segmentation Pipeline for Scalable Neuropathological Analysis.IEEE access : practical innovations, open solutions · 2022Article
- H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images.Frontiers in medicine · 2022Article
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Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Supervised learning methods are commonly applied in medical image analysis. However, the success of these approaches is highly dependent on the availability of large manually detailed annotated dataset. Thus an automatic refined segmentation of whole-slide image (WSI) is significant to alleviate the annotation workload of pathologists. But most of the current ways can only output a rough prediction of lesion areas and consume much time in each slide. In this paper, we propose a fast and refined cancer regions segmentation framework v3_DCNN, which first preselects tumor regions using a classification model Inception-v3 and then employs a semantic segmentation model DCNN for refined segmentation. Our framework can generate a dense likelihood heatmap with the 1/8 side of original WSI in 11.5 minutes on the Camelyon16 dataset, which saves more than one hour for each WSI compared with the initial DCNN model. Experimental results show that our approach achieves a higher FROC score 83.5% with the champion's method of Camelyon16 challenge 80.7%. Based on v3 DCNN model, we further automatically produce heatmap of WSI and extract polygons of lesion regions for doctors, which is very helpful for their pathological diagnosis, detailed annotation and thus contributes to developing a more powerful deep learning model.
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