4 papers
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…
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…
Prostate-Specific Foundation Models for Enhanced Detection of Clinically Significant Cancer
Jeong Hoon Lee, Cynthia Xinran Li, Hassan Jahanandish +14
Accurate prostate cancer diagnosis remains challenging. Even when using MRI, radiologists exhibit low specificity and significant inter-observer variability, leading to potential d…
Multimodal MRI-Ultrasound AI for Prostate Cancer Detection Outperforms Radiologist MRI Interpretation: A Multi-Center Study
Hassan Jahanandish, Shengtian Sang, Cynthia Xinran Li +6
Pre-biopsy magnetic resonance imaging (MRI) is increasingly used to target suspicious prostate lesions. This has led to artificial intelligence (AI) applications improving MRI-base…