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Found 8 papers, 1 papers with code
Reconsidering evaluation practices in modular systems: On the propagation of errors in MRI prostate cancer detection
Magnetic resonance imaging has evolved as a key component for prostate cancer (PCa) detection, substantially increasing the radiologist workload.
Leveraging multi-view data without annotations for prostate MRI segmentation: A contrastive approach
Further, our approach shows good external volumetric generalization in an in-house dataset when tested with multi-view data (2. 76+-1. 89% compared to 3. 92+-3. 31%, P=. 002), showing the feasibility of exploiting non-annotated multi-view data through contrastive learning whilst providing flexibility at deployment in the event of missing views.
Prostate Age Gap (PAG): An MRI surrogate marker of aging for prostate cancer detection
Assessment: Chronological age was defined as the age of the participant at the time of the visit and used to train the deep learning model to predict the age of the patient.
Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence Assisted Cancer Diagnosis
The potential of artificial intelligence (AI) in digital pathology is limited by technical inconsistencies in the production of whole slide images (WSIs), leading to degraded AI performance and posing a challenge for widespread clinical application as fine-tuning algorithms for each new site is impractical.
Using deep learning to detect patients at risk for prostate cancer despite benign biopsies
As a proof-of-principle, we developed and validated a deep convolutional neural network model to distinguish between morphological patterns in benign prostate biopsy whole slide images from men with and without established cancer.
Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression based convolutional neural networks
Molecular phenotyping by gene expression profiling is common in contemporary cancer research and in molecular diagnostics.
Detection of Perineural Invasion in Prostate Needle Biopsies with Deep Neural Networks
Results: For the detection of PNI in prostate biopsy cores the network had an estimated area under the receiver operating characteristics curve of 0. 98 (95% CI 0. 97-0. 99) based on 106 PNI positive cores and 1, 652 PNI negative cores in the independent test set.
Pathologist-Level Grading of Prostate Biopsies with Artificial Intelligence
We additionally evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology.
