ArticleJournal of digital imaging2019
A Deep Learning-Based Approach for the Detection and Localization of Prostate Cancer in T2 Magnetic Resonance Images.
Article in Journal of digital imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.
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Who cites it
40 citing papers in PubMed, 2 syntheses or guidelines pooled it, 117 citations in OpenAlex.
- Algorithms on the rise: a machine learning-driven survey of prostate cancer literature.Frontiers in oncology · 2025Pooled it
- Interactive, Up-to-date Meta-Analysis of MRI in the Management of Men with Suspected Prostate Cancer.Journal of digital imaging · 2020Pooled it
- Real-world assessment of a deep learning neural network algorithm for prostate cancer detection in MRI using true de novo data.Medical physics · 2026Article
- Unveiling the role of harmonization on clinically significant prostate cancer detection using MRI.Scientific reports · 2025Article
- Multimodal imaging deep learning model for predicting extraprostatic extension in prostate cancer using MpMRI and 18 F-PSMA-PET/CT.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Integration of magnetic resonance imaging and deep learning for prostate cancer detection: a systematic review.American journal of clinical and experimental urology · 2025Review
- Advancing Prostate Cancer Diagnosis: A Deep Learning Approach for Enhanced Detection in MRI Images.Diagnostics (Basel, Switzerland) · 2024Article
- Catalyzing Precision Medicine: Artificial Intelligence Advancements in Prostate Cancer Diagnosis and Management.Cancers · 2024Review
- T2-weighted imaging-based deep-learning method for noninvasive prostate cancer detection and Gleason grade prediction: a multicenter study.Insights into imaging · 2024Article
- Reliable Prostate Cancer Risk Mapping From MRI Using Targeted and Systematic Core Needle Biopsy Histopathology.IEEE transactions on bio-medical engineering · 2024Article
- Article
- Prostate Cancer Detection from MRI Using Efficient Feature Extraction with Transfer Learning.Prostate cancer · 2024Review
- Addressing image misalignments in multi-parametric prostate MRI for enhanced computer-aided diagnosis of prostate cancer.Scientific reports · 2023Article
- Deep Learning Algorithm for Tumor Segmentation and Discrimination of Clinically Significant Cancer in Patients with Prostate Cancer.Current oncology (Toronto, Ont.) · 2023Article
- Deep learning-based dominant index lesion segmentation for MR-guided radiation therapy of prostate cancer.Medical physics · 2023Article
- Anatomically guided self-adapting deep neural network for clinically significant prostate cancer detection on bi-parametric MRI: a multi-center study.Insights into imaging · 2023Article
- INTEGRATING PROSTATE SPECIFIC ANTIGEN DENSITY BIOMARKER INTO DEEP LEARNING PROSTATE MRI LESION SEGMENTATION MODELS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
- A classifier model for prostate cancer diagnosis using CNNs and transfer learning with multi-parametric MRI.Frontiers in oncology · 2023Article
- A New Framework for Precise Identification of Prostatic Adenocarcinoma.Sensors (Basel, Switzerland) · 2022Article
- Machine Learning in Prostate MRI for Prostate Cancer: Current Status and Future Opportunities.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We address the problem of prostate lesion detection, localization, and segmentation in T2W magnetic resonance (MR) images. We train a deep convolutional encoder-decoder architecture to simultaneously segment the prostate, its anatomical structure, and the malignant lesions. To incorporate the 3D contextual spatial information provided by the MRI series, we propose a novel 3D sliding window approach, which preserves the 2D domain complexity while exploiting 3D information. Experiments on data from 19 patients provided for the public by the Initiative for Collaborative Computer Vision Benchmarking (I2CVB) show that our approach outperforms traditional pattern recognition and machine learning approaches by a significant margin. Particularly, for the task of cancer detection and localization, the system achieves an average AUC of 0.995, an accuracy of 0.894, and a recall of 0.928. The proposed mono-modal deep learning-based system performs comparably to other multi-modal MR-based systems. It could improve the performance of a radiologist in prostate cancer diagnosis and treatment planning.
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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.