ArticleIEEE transactions on medical imaging2019
Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images.
Article in IEEE transactions on medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed, 132 citations in OpenAlex.
- BiChemoCLAM: a weakly supervised multimodal framework for chemotherapy response prediction.Briefings in bioinformatics · 2026Article
- Deep learning models built from PSMA PET of the primary tumor can predict synchronous and metachronous prostate cancer metastases.PloS one · 2026Article
- Article
- A foundation model-based multi-instance learning framework for accurate prediction of lymph node metastasis in prostate cancer from whole slide images.Frontiers in oncology · 2026Article
- Enhanced YOLOv8 with DWR-DRB and SPD-Conv for Mechanical Wear Fault Diagnosis in Aero-Engines.Sensors (Basel, Switzerland) · 2025Article
- Exploring the feasibility of AI-based analysis of histopathological variability in salivary gland tumours.Scientific reports · 2025Article
- Generative Artificial Intelligence in Prostate Cancer Imaging.Balkan medical journal · 2025Review
- Enhanced hierarchical attention mechanism for mixed MIL in automatic Gleason grading and scoring.Scientific reports · 2025Article
- Varying pixel resolution significantly improves deep learning-based carotid plaque histology segmentation.Scientific reports · 2025Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- A selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancer.Journal of pathology informatics · 2024Article
- An Artificial Intelligent System for Prostate Cancer Diagnosis in Whole Slide Images.Journal of medical systems · 2024Article
- Harnessing artificial intelligence for prostate cancer management.Cell reports. Medicine · 2024Review
- [Chinese expert consensus on the technical and clinical practice specifications of artificial intelligence assisted morphology examination of blood cells (2024)].Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi · 2024Article
- Transfer Learning with Pretrained Convolutional Neural Network for Automated Gleason Grading of Prostate Cancer Tissue Microarrays.Journal of medical signals and sensors · 2024Article
- BlobCUT: A Contrastive Learning Method to Support Small Blob Detection in Medical Imaging.Bioengineering (Basel, Switzerland) · 2023Article
- Deep learning-based methods for classification of microsatellite instability in endometrial cancer from HE-stained pathological images.Journal of cancer research and clinical oncology · 2023Article
- Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- An Assisted Diagnosis Model for Cancer Patients Based on Federated Learning.Frontiers in oncology · 2022Article
- Computational pathology in ovarian cancer.Frontiers in oncology · 2022Review
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Prostate cancer is the most common and second most deadly form of cancer in men in the United States. The classification of prostate cancers based on Gleason grading using histological images is important in risk assessment and treatment planning for patients. Here, we demonstrate a new region-based convolutional neural network framework for multi-task prediction using an epithelial network head and a grading network head. Compared with a single-task model, our multi-task model can provide complementary contextual information, which contributes to better performance. Our model is achieved a state-of-the-art performance in epithelial cells detection and Gleason grading tasks simultaneously. Using fivefold cross-validation, our model is achieved an epithelial cells detection accuracy of 99.07% with an average area under the curve of 0.998. As for Gleason grading, our model is obtained a mean intersection over union of 79.56% and an overall pixel accuracy of 89.40%.
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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.