Search Results for author: Natasha Thorley

Found 3 papers, 2 papers with code

T2-Only Prostate Cancer Prediction by Meta-Learning from Bi-Parametric MR Imaging

1 code implementation11 Nov 2024 Weixi Yi, Yipei Wang, Natasha Thorley, Alexander Ng, Shonit Punwani, Veeru Kasivisvanathan, Dean C. Barratt, Shaheer Ullah Saeed, Yipeng Hu

Current imaging-based prostate cancer diagnosis requires both MR T2-weighted (T2w) and diffusion-weighted imaging (DWI) sequences, with additional sequences for potentially greater accuracy improvement.

Meta-Learning

AI-assisted prostate cancer detection and localisation on biparametric MR by classifying radiologist-positives

no code implementations30 Oct 2024 Xiangcen Wu, Yipei Wang, Qianye Yang, Natasha Thorley, Shonit Punwani, Veeru Kasivisvanathan, Ester Bonmati, Yipeng Hu

Based on the presented experiments from two clinical data sets, consisting of histopathology-labelled MR images from more than 800 and 500 patients in the respective UCLA and UCL PROMIS studies, we show that the proposed strategy can improve the diagnostic accuracy, by augmenting the radiologist reading of the MR imaging.

Diagnostic Specificity

Poisson Ordinal Network for Gleason Group Estimation Using Bi-Parametric MRI

1 code implementation8 Jul 2024 Yinsong Xu, Yipei Wang, Ziyi Shen, Iani J. M. B. Gayo, Natasha Thorley, Shonit Punwani, Aidong Men, Dean Barratt, Qingchao Chen, Yipeng Hu

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.

Contrastive Learning

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