4 citations · 9 across the 10 of their papers we have counts for
10 papers
SAMRI-3D: Adapting SAM2 for 3D MRI Segmentation with Global Volume Tokens
Zhao Wang, Wei Dai, Hongfu Sun +2
Foundation models such as Segment Anything Model 2 (SAM2) have transformed natural-image and video segmentation, and recent work has begun adapting them to medical imaging. These a…
SAMRI: Segment Any MRI
Zhao Wang, Wei Dai, Thuy Thanh Dao +4
Summary: SAMRI is an MRI-specialized adaptation of the Segment Anything Model achieving superior whole-body MRI segmentation, particularly for small and clinically critical structu…
Evidence-aware multi-modal data fusion and its application to total knee replacement prediction
Xinwen Liu, Jing Wang, S. Kevin Zhou +2
Deep neural networks have been widely studied for predicting a medical condition, such as total knee replacement (TKR). It has shown that data of different modalities, such as imag…
Explainable Semantic Medical Image Segmentation with Style
Wei Dai, Siyu Liu, Craig B. Engstrom +1
Semantic medical image segmentation using deep learning has recently achieved high accuracy, making it appealing to clinical problems such as radiation therapy. However, the lack o…
Automated anomaly-aware 3D segmentation of bones and cartilages in knee MR images from the Osteoarthritis Initiative
Boyeong Woo, Craig Engstrom, William Baresic +3
In medical image analysis, automated segmentation of multi-component anatomical structures, which often have a spectrum of potential anomalies and pathologies, is a challenging tas…
Structure Guided Manifolds for Discovery of Disease Characteristics
Siyu Liu, Linfeng Liu, Xuan Vinh +4
In medical image analysis, the subtle visual characteristics of many diseases are challenging to discern, particularly due to the lack of paired data. For example, in mild Alzheime…
Transformer Compressed Sensing via Global Image Tokens
Marlon Bran Lorenzana, Craig Engstrom, Shekhar S. Chandra
Convolutional neural networks (CNN) have demonstrated outstanding Compressed Sensing (CS) performance compared to traditional, hand-crafted methods. However, they are broadly limit…
Automated volumetric and statistical shape assessment of cam-type morphology of the femoral head-neck region from 3D magnetic resonance images
Jessica M. Bugeja, Ying Xia, Shekhar S. Chandra +7
Femoroacetabular impingement (FAI) cam morphology is routinely assessed using two-dimensional alpha angles which do not provide specific data on cam size characteristics. The purpo…
CAN3D: Fast 3D Medical Image Segmentation via Compact Context Aggregation
Wei Dai, Boyeong Woo, Siyu Liu +6
Direct automatic segmentation of objects from 3D medical imaging, such as magnetic resonance (MR) imaging, is challenging as it often involves accurately identifying a number of in…
Generalisable 3D Fabric Architecture for Streamlined Universal Multi-Dataset Medical Image Segmentation
Siyu Liu, Wei Dai, Craig Engstrom +4
Data scarcity is common in deep learning models for medical image segmentation. Previous works proposed multi-dataset learning, either simultaneously or via transfer learning to ex…