3 papers
cs.CV2026
MUST-PET: MUltimodal Self-supervised learning across Tracers for whole-body PET/CT-based lesion segmentation
Bashirul Azam Biswas, Amartya Bhattacharya, Biratal Raj Wagle +3
Deep learning-based whole-body PET-CT lesion segmentation can support cancer staging, treatment planning, and response assessment, but generalization is limited by scarce annotatio…
cs.CV2026
Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets
Biratal Raj Wagle, Bashirul Azam Biswas, Grant Chau +5
Automated lesion segmentation in whole-body PET/CT imaging can assist clinicians with cancer detection, staging, and treatment planning across radiotracers and cancer types. Howeve…
cs.CV2026
Improving PET/CT-Based Whole-Body Lesion Segmentation Using Prediction Uncertainty-Augmented Models
Bashirul Azam Biswas, Biratal Raj Wagle, Zhihan Yang +4
Accurate lesion segmentation from whole-body Positron Emission Tomography (PET)/Computed Tomography (CT) scans is essential for cancer staging and treatment planning. PET provides…