51 citations · 138 across the 42 of their papers we have counts for
13 papers · 1 filter
Contrastive uncertainty learning for robust multi-structure segmentation in lower-pelvic MR
Bilal Sidiqi, Yipei Wang, Wen Yan +3
Boundary delineation of anatomical structures in prostate MRI is often compromised by regional ambiguity. Automated segmentation methods relying on global overlap loss functions te…
Continual pretraining Prostate MRI ViT Encoder for task alignment
Bilal A. Sidiqi, Yipei Wang, Shaheer U. Saeed +3
Building foundation models for MRI has emerged as a promising direction for advancing medical image analysis and improving accessibility of task specific models. Existing approache…
Bayesian adaptively-weighted ensembles for few-shot abdominal segmentation
Abbas Al-Sabbagh, Shalom F. Mushtaq, Tomás M. da Silva +7
Few-shot learning has emerged as a promising approach for anatomical segmentation when labelled data are scarce. However, different few-shot learning algorithms exhibit complementa…
Active few-shot segmentation by reinforcing data selection
Chenlan Zhao, Benny Wong, Timothy F. Lundberg +8
Few-shot learning enables medical image segmentation models to adapt to new tasks using only a small number of labelled examples. However, adaptation performance depends strongly o…
Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability
Qi Li, Yuliang Huang, Shaheer U. Saeed +7
Deep learning-based medical image segmentation models are trained using annotations that exhibit systematic bias and variability across raters. While probabilistic multi-rater appr…
Learning to Distort: Weakly-Supervised Image Quality Transfer for Prostate DWI Correction
YuCheng Tang, Wen Yan, Alexander Ng +13
Single-shot echo-planar prostate diffusion-weighted imaging (DWI) is frequently complicated by geometric distortions, which impact the ability to derive reliable diagnoses from suc…
Maximizing T2-Only Prostate Cancer Localization from Expected Diffusion Weighted Imaging
Weixi Yi, Yipei Wang, Wen Yan +10
Multiparametric MRI is increasingly recommended as a first-line noninvasive approach to detect and localize prostate cancer, requiring at minimum diffusion-weighted (DWI) and T2-we…
ProFound: A moderate-sized vision foundation model for multi-task prostate imaging
Yipei Wang, Yinsong Xu, Weixi Yi +11
Many diagnostic and therapeutic clinical tasks for prostate cancer increasingly rely on multi-parametric MRI. Automating these tasks is challenging because they necessitate expert…
Retrieving Patient-Specific Radiomic Feature Sets for Transparent Knee MRI Assessment
Yaxi Chen, Simin Ni, Jingjing Zhang +7
Classical radiomic features are designed to quantify image appearance and intensity patterns. Compared with end-to-end deep learning (DL) models trained for disease classification,…
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…
Understanding the Transfer Limits of Vision Foundation Models
Shiqi Huang, Yipei Wang, Natasha Thorley +8
Foundation models leverage large-scale pretraining to capture extensive knowledge, demonstrating generalization in a wide range of language tasks. By comparison, vision foundation…
Radiomics-Integrated Deep Learning with Hierarchical Loss for Osteosarcoma Histology Classification
Yaxi Chen, Zi Ye, Shaheer U. Saeed +4
Osteosarcoma (OS) is an aggressive primary bone malignancy. Accurate histopathological assessment of viable versus non-viable tumor regions after neoadjuvant chemotherapy is critic…
Interpretability and Individuality in Knee MRI: Patient-Specific Radiomic Fingerprint with Reconstructed Healthy Personas
Yaxi Chen, Simin Ni, Shuai Li +6
For automated assessment of knee MRI scans, both accuracy and interpretability are essential for clinical use and adoption. Traditional radiomics rely on predefined features chosen…