ArticleInternational journal of biomedical imaging2024
Detecting MRI-Invisible Prostate Cancers Using a Weakly Supervised Deep Learning Model.
Article in International journal of biomedical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Prostate cancer detection using modified transformer with optimal feature selection from MRI images.Scientific reports · 2026Article
- Benchmarking multiple instance learning architectures from patches to pathology for prostate cancer detection and grading using attention-based weak supervision.Scientific reports · 2026Article
- Prostate Cancer Classification Using Quantum Machine Learning on Multi-parametric MRI.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Editorial for the Special Issue "Advances in Medical Image Processing, Segmentation, and Classification".Diagnostics (Basel, Switzerland) · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: MRI is an important tool for accurate detection and targeted biopsy of prostate lesions. However, the imaging appearances of some prostate cancers are similar to those of the surrounding normal tissue on MRI, which are referred to as MRI-invisible prostate cancers (MIPCas). The detection of MIPCas remains challenging and requires extensive systematic biopsy for identification. In this study, we developed a weakly supervised UNet (WSUNet) to detect MIPCas. Methods: The study included 777 patients (training set: 600; testing set: 177), all of them underwent comprehensive prostate biopsies using an MRI-ultrasound fusion system. MIPCas were identified in MRI based on the Gleason grade (≥7) from known systematic biopsy results. Results: The WSUNet model underwent validation through systematic biopsy in the testing set with an AUC of 0.764 (95% CI: 0.728-0.798). Furthermore, WSUNet exhibited a statistically significant precision improvement of 91.3% ( Conclusions: In conclusion, the proposed WSUNet could effectively detect MIPCas, thereby reducing unnecessary biopsies.
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Registered trials
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