2 citations · 4 across the 11 of their papers we have counts for
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Promptable segmentation with region exploration enables minimal-effort expert-level prostate cancer delineation
Junqing Yang, Natasha Thorley, Ahmed Nadeem Abbasi +4
Purpose: Accurate segmentation of prostate cancer on magnetic resonance (MR) images is crucial for planning image-guided interventions such as targeted biopsies, cryoablation, and…
T2-Only Prostate Cancer Prediction by Meta-Learning from Bi-Parametric MR Imaging
Weixi Yi, Yipei Wang, Natasha Thorley +6
Current imaging-based prostate cancer diagnosis requires both MR T2-weighted (T2w) and diffusion-weighted imaging (DWI) sequences, with additional sequences for potentially greater…
AI-assisted prostate cancer detection and localisation on biparametric MR by classifying radiologist-positives
Xiangcen Wu, Yipei Wang, Qianye Yang +5
Prostate cancer diagnosis through MR imaging have currently relied on radiologists' interpretation, whilst modern AI-based methods have been developed to detect clinically signific…
Poisson Ordinal Network for Gleason Group Estimation Using Bi-Parametric MRI
Yinsong Xu, Yipei Wang, Ziyi Shen +7
The Gleason groups serve as the primary histological grading system for prostate cancer, providing crucial insights into the cancer's potential for growth and metastasis. In clinic…