12 citations · 17 across the 4 of their papers we have counts for
4 papers
Performance Deterioration of Deep Learning Models after Clinical Deployment: A Case Study with Auto-segmentation for Definitive Prostate Cancer Radiotherapy
Biling Wang, Michael Dohopolski, Ti Bai +10
We evaluated the temporal performance of a deep learning (DL) based artificial intelligence (AI) model for auto segmentation in prostate radiotherapy, seeking to correlate its effi…
PSA-Net: Deep Learning based Physician Style-Aware Segmentation Network for Post-Operative Prostate Cancer Clinical Target Volume
Anjali Balagopal, Howard Morgan, Michael Dohopoloski +9
Automatic segmentation of medical images with DL algorithms has proven to be highly successful. With most of these algorithms, inter-observer variation is an acknowledged problem,…
Dosimetric impact of physician style variations in contouring CTV for post-operative prostate cancer: A deep learning-based simulation study
Anjali Balagopal, Dan Nguyen, Maryam Mashayekhi +6
Inter-observer variation is a significant problem in clinical target volume(CTV) segmentation in postoperative settings, where there is no gross tumor present. In this scenario, th…
A deep learning-based framework for segmenting invisible clinical target volumes with estimated uncertainties for post-operative prostate cancer radiotherapy
Anjali Balagopal, Dan Nguyen, Howard Morgan +9
In post-operative radiotherapy for prostate cancer, the cancerous prostate gland has been surgically removed, so the clinical target volume (CTV) to be irradiated encompasses the m…