ArticleRadiation oncology (London, England)2022
Explainable AI for CNN-based prostate tumor segmentation in multi-parametric MRI correlated to whole mount histopathology.
Article in Radiation oncology (London, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The role of explainable artificial intelligence in disease prediction: a systematic literature review and future research directions.BMC medical informatics and decision making · 2025Pooled it
- MS-GATOR: multi-scale graph attention with topological reasoning for segmentation and classification of prostate cancer based on Gleason scores using histopathology images.Scientific reports · 2026Article
- Convolutional neural networks using preoperative CT to predict short-term recurrence after incisional hernia repair.Scientific reports · 2026Article
- Application and prospect of artificial intelligence in diagnostic imaging of prostate cancer.NPJ digital medicine · 2026Review
- A reliable and explainable deep learning framework for clinical-grade endocrine disorder risk prediction and decision support.Frontiers in artificial intelligence · 2026Article
- Whole-mount histopathology as spatial ground truth for artificial intelligence in prostate cancer: a structured narrative review of techniques, models, and translational gaps.American journal of clinical and experimental urology · 2026Review
- Pathologist-like explainable AI for interpretable Gleason grading in prostate cancer.Nature communications · 2025Article
- Deep Learning Techniques for Prostate Cancer Analysis and Detection: Survey of the State of the Art.Journal of imaging · 2025Review
- Automatic Characterization of Prostate Suspect Lesions on T2-Weighted Image Acquisitions Using Texture Features and Machine-Learning Methods: A Pilot Study.Diagnostics (Basel, Switzerland) · 2025Article
- Automatic segmentation of white matter lesions on multi-parametric MRI: convolutional neural network versus vision transformer.BMC neurology · 2025Article
- Artificial intelligence in prostate cancer: navigating the new frontier of precision uro-oncology.American journal of clinical and experimental urology · 2025Review
- Comparison of data fusion strategies for automated prostate lesion detection using mpMRI correlated with whole mount histology.Radiation oncology (London, England) · 2024Article
- ProLesA-Net: A multi-channel 3D architecture for prostate MRI lesion segmentation with multi-scale channel and spatial attentions.Patterns (New York, N.Y.) · 2024Article
- Catalyzing Precision Medicine: Artificial Intelligence Advancements in Prostate Cancer Diagnosis and Management.Cancers · 2024Review
- Advantage of whole-mount histopathology in prostate cancer: current applications and future prospects.BMC cancer · 2024Review
- Algorithms for classification of sequences and segmentation of prostate gland: an external validation study.Abdominal radiology (New York) · 2024Article
- Kernel-weighted contribution: a method of visual attribution for 3D deep learning segmentation in medical imaging.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Deep learning-based dominant index lesion segmentation for MR-guided radiation therapy of prostate cancer.Medical physics · 2023Article
- Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics.Investigative radiology · 2023Review
- Review
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9 authors.
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Abstract
Automatic prostate tumor segmentation is often unable to identify the lesion even if multi-parametric MRI data is used as input, and the segmentation output is difficult to verify due to the lack of clinically established ground truth images. In this work we use an explainable deep learning model to interpret the predictions of a convolutional neural network (CNN) for prostate tumor segmentation. The CNN uses a U-Net architecture which was trained on multi-parametric MRI data from 122 patients to automatically segment the prostate gland and prostate tumor lesions. In addition, co-registered ground truth data from whole mount histopathology images were available in 15 patients that were used as a test set during CNN testing. To be able to interpret the segmentation results of the CNN, heat maps were generated using the Gradient Weighted Class Activation Map (Grad-CAM) method. The CNN achieved a mean Dice Sorensen Coefficient 0.62 and 0.31 for the prostate gland and the tumor lesions -with the radiologist drawn ground truth and 0.32 with whole-mount histology ground truth for tumor lesions. Dice Sorensen Coefficient between CNN predictions and manual segmentations from MRI and histology data were not significantly different. In the prostate the Grad-CAM heat maps could differentiate between tumor and healthy prostate tissue, which indicates that the image information in the tumor was essential for the CNN segmentation.
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