Prostate Cancer Malignancy Detection and localization from mpMRI using auto-Deep Learning: One Step Closer to Clinical Utilization
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Abstract (arXiv)
Automatic diagnosis of malignant prostate cancer patients from mpMRI has been studied heavily in the past years. Model interpretation and domain drift have been the main road blocks for clinical utilization. As an extension from our previous work where we trained a customized convolutional neural network on a public cohort with 201 patients and the cropped 2D patches around the region of interest were used as the input, the cropped 2.5D slices of the prostate glands were used as the input, and the optimal model were searched in the model space using autoKeras. Something different was peripheral zone (PZ) and central gland (CG) were trained and tested separately, the PZ detector and CG detector were demonstrated effectively in highlighting the most suspicious slices out of a sequence, hopefully to greatly ease the workload for the physicians.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 17 references that are in the map. First posted on arXiv 13 Jun 2022.
Builds on
5 of 17 references.
References · 5
- 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,182
- 2017Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?cited by 220
- 2019Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screeningcited by 94
- 2016Very Deep Convolutional Networks for End-to-End Speech Recognitioncited by 34
- 2017Prostate Cancer Diagnosis using Deep Learning with 3D Multiparametric MRIcited by 7
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Citing papers per year: 2026: 1.
1 paper.