ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2020
An Automated Two-step Pipeline for Aggressive Prostate Lesion Detection from Multi-parametric MR Sequence.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- ProstAtlasDiff: Prostate cancer detection on MRI using Diffusion Probabilistic Models guided by population spatial cancer atlases.Medical image analysis · 2025Article
- Catalyzing Precision Medicine: Artificial Intelligence Advancements in Prostate Cancer Diagnosis and Management.Cancers · 2024Review
- Addressing image misalignments in multi-parametric prostate MRI for enhanced computer-aided diagnosis of prostate cancer.Scientific reports · 2023Article
- Semisupervised Learning with Report-guided Pseudo Labels for Deep Learning-based Prostate Cancer Detection Using Biparametric MRI.Radiology. Artificial intelligence · 2023Article
- Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review.Frontiers in medicine · 2023Review
- Bridging the gap between prostate radiology and pathology through machine learning.Medical physics · 2022Article
- Computational Detection of Extraprostatic Extension of Prostate Cancer on Multiparametric MRI Using Deep Learning.Cancers · 2022Article
- Comparative performance of fully-automated and semi-automated artificial intelligence methods for the detection of clinically significant prostate cancer on MRI: a systematic review.Insights into imaging · 2022Review
- Fully automated detection and localization of clinically significant prostate cancer on MR images using a cascaded convolutional neural network.Frontiers in oncology · 2022Article
- Selective identification and localization of indolent and aggressive prostate cancers via CorrSigNIA: an MRI-pathology correlation and deep learning framework.Medical image analysis · 2022Article
- Automated detection of aggressive and indolent prostate cancer on magnetic resonance imaging.Medical physics · 2021Article
- Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review.Diagnostics (Basel, Switzerland) · 2021Review
- A review of artificial intelligence in prostate cancer detection on imaging.Therapeutic advances in urologyReview
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Authors and funding
4 authors.
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Abstract
A substantial percentage of prostate cancer cases are overdiagnosed and overtreated due to the challenge in deter- mining aggressiveness. Multi-parametric MR is a powerful imaging technique to capture distinct characteristics of prostate lesions that are informative for aggressiveness assessment. However, manual interpretation requires a high level of expertise, is time-consuming, and significant inter-observer variation exists for radiologists. We propose a completely automated approach to assessing pixel-level aggressiveness of prostate cancer in multi-parametric MRI. Our model efficiently combines traditional computer vision and deep learning algorithms, to remove reliance on manual features, prostate segmentation, and prior lesion detection and identified optimal combinations of MR pulse sequences for assessment. Using ADC and DWI, our proposed model achieves ROC-AUC of 0.86 and ROC-AUC of 0.88 for the diagnosis of aggressive and non-aggressive prostate lesions, respectively. In performing pixel-level clas- sification, our model's classifications are easily interpretable and allow clinicians to infer localized analyses of the lesion.
Identifiers
32477677PMC7233091What OpenQuestion holds
Registered trials
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