14 citations · 62 across the 29 of their papers we have counts for
29 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…
Anatomical grounding pre-training for medical phrase grounding
Wenjun Zhang, Shakes Chandra, Aaron Nicolson
Medical Phrase Grounding (MPG) maps radiological findings described in medical reports to specific regions in medical images. The primary obstacle hindering progress in MPG is the…
TriFormer: A Multi-modal Transformer Framework For Mild Cognitive Impairment Conversion Prediction
Linfeng Liu, Junyan Lyu, Siyu Liu +3
The prediction of mild cognitive impairment (MCI) conversion to Alzheimer's disease (AD) is important for early treatment to prevent or slow the progression of AD. To accurately pr…
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…
Towards Trustable Skin Cancer Diagnosis via Rewriting Model's Decision
Siyuan Yan, Zhen Yu, Xuelin Zhang +5
Deep neural networks have demonstrated promising performance on image recognition tasks. However, they may heavily rely on confounding factors, using irrelevant artifacts or bias w…
AliasNet: Alias Artefact Suppression Network for Accelerated Phase-Encode MRI
Marlon E. Bran Lorenzana, Shekhar S. Chandra, Feng Liu
Sparse reconstruction is an important aspect of MRI, helping to reduce acquisition time and improve spatial-temporal resolution. Popular methods are based mostly on compressed sens…
NBC-Softmax : Darkweb Author fingerprinting and migration tracking
Gayan K. Kulatilleke, Shekhar S. Chandra, Marius Portmann
Metric learning aims to learn distances from the data, which enhances the performance of similarity-based algorithms. An author style detection task is a metric learning problem, w…
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…
Cascaded Multi-Modal Mixing Transformers for Alzheimer's Disease Classification with Incomplete Data
Linfeng Liu, Siyu Liu, Lu Zhang +3
Accurate medical classification requires a large number of multi-modal data, and in many cases, different feature types. Previous studies have shown promising results when using mu…
Efficient block contrastive learning via parameter-free meta-node approximation
Gayan K. Kulatilleke, Marius Portmann, Shekhar S. Chandra
Contrastive learning has recently achieved remarkable success in many domains including graphs. However contrastive loss, especially for graphs, requires a large number of negative…
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…
Skin Lesion Recognition with Class-Hierarchy Regularized Hyperbolic Embeddings
Zhen Yu, Toan Nguyen, Yaniv Gal +7
In practice, many medical datasets have an underlying taxonomy defined over the disease label space. However, existing classification algorithms for medical diagnoses often assume…
SCGC : Self-Supervised Contrastive Graph Clustering
Gayan K. Kulatilleke, Marius Portmann, Shekhar S. Chandra
Graph clustering discovers groups or communities within networks. Deep learning methods such as autoencoders (AE) extract effective clustering and downstream representations but ca…
Undersampled MRI Reconstruction with Side Information-Guided Normalisation
Xinwen Liu, Jing Wang, Cheng Peng +3
Magnetic resonance (MR) images exhibit various contrasts and appearances based on factors such as different acquisition protocols, views, manufacturers, scanning parameters, etc. T…
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…
FDGATII : Fast Dynamic Graph Attention with Initial Residual and Identity Mapping
Gayan K. Kulatilleke, Marius Portmann, Ryan Ko +1
While Graph Neural Networks have gained popularity in multiple domains, graph-structured input remains a major challenge due to (a) over-smoothing, (b) noisy neighbours (heterophil…
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…
Bespoke Fractal Sampling Patterns for Discrete Fourier Space via the Kaleidoscope Transform
Jacob M. White, Stuart Crozier, Shekhar S. Chandra
Sampling strategies are important for sparse imaging methodologies, especially those employing the discrete Fourier transform (DFT). Chaotic sensing is one such methodology that em…
Deep Simultaneous Optimisation of Sampling and Reconstruction for Multi-contrast MRI
Xinwen Liu, Jing Wang, Fangfang Tang +3
MRI images of the same subject in different contrasts contain shared information, such as the anatomical structure. Utilizing the redundant information amongst the contrasts to sub…
Manipulating Medical Image Translation with Manifold Disentanglement
Siyu Liu, Jason A. Dowling, Craig Engstrom +3
Medical image translation (e.g. CT to MR) is a challenging task as it requires I) faithful translation of domain-invariant features (e.g. shape information of anatomical structures…
Automatic lesion detection, segmentation and characterization via 3D multiscale morphological sifting in breast MRI
Hang Min, Darryl McClymont, Shekhar S. Chandra +2
Previous studies on computer aided detection/diagnosis (CAD) in 4D breast magnetic resonance imaging (MRI) regard lesion detection, segmentation and characterization as separate ta…