31 citations · 92 across the 21 of their papers we have counts for
22 papers
When does Subgroup Balance Matter? Predicting Performance Sensitivity from Latent Separability
Anissa Alloula, Charles Jones, Zuzanna Wakefield-Skorniewska +2
Unequal representation of demographic groups in training data poses challenges to model generalisation across populations. Standard practice assumes that balancing subgroup represe…
Emerging Semantic Segmentation from Positive and Negative Coarse Label Learning
Le Zhang, Fuping Wu, Arun Thirunavukarasu +3
Large annotated datasets are vital for training segmentation models, but pixel-level labeling is time-consuming, error-prone, and often requires scarce expert annotators, especiall…
Interpretable Rheumatoid Arthritis Scoring via Anatomy-aware Multiple Instance Learning
Zhiyan Bo, Laura C. Coates, Bartlomiej W. Papiez
The Sharp/van der Heijde (SvdH) score has been widely used in clinical trials to quantify radiographic damage in Rheumatoid Arthritis (RA), but its complexity has limited its adopt…
Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis
Zuzanna Skorniewska, Bartlomiej W. Papiez
The adoption of neural network models in medical imaging has been constrained by strict privacy regulations, limited data availability, high acquisition costs, and demographic bias…
Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST
Fuping Wu, Bartlomiej W. Papiez
Foundation models are widely employed in medical image analysis, due to their high adaptability and generalizability for downstream tasks. With the increasing number of foundation…
KneeXNeT: An Ensemble-Based Approach for Knee Radiographic Evaluation
Nicharee Srikijkasemwat, Soumya Snigdha Kundu, Fuping Wu +1
Knee osteoarthritis (OA) is the most common joint disorder and a leading cause of disability. Diagnosing OA severity typically requires expert assessment of X-ray images and is com…
SpineFM: Leveraging Foundation Models for Automatic Spine X-ray Segmentation
Samuel J. Simons, Bartłomiej W. Papież
This paper introduces SpineFM, a novel pipeline that achieves state-of-the-art performance in the automatic segmentation and identification of vertebral bodies in cervical and lumb…
Fool Me Once? Contrasting Textual and Visual Explanations in a Clinical Decision-Support Setting
Maxime Kayser, Bayar Menzat, Cornelius Emde +7
The growing capabilities of AI models are leading to their wider use, including in safety-critical domains. Explainable AI (XAI) aims to make these models safer to use by making th…
Deformation-Recovery Diffusion Model (DRDM): Instance Deformation for Image Manipulation and Synthesis
Jian-Qing Zheng, Yuanhan Mo, Yang Sun +5
In medical imaging, the diffusion models have shown great potential for synthetic image generation tasks. However, these approaches often lack the interpretable connections between…
Deep Learning Models to Automate the Scoring of Hand Radiographs for Rheumatoid Arthritis
Zhiyan Bo, Laura C. Coates, Bartlomiej W. Papiez
The van der Heijde modification of the Sharp (SvdH) score is a widely used radiographic scoring method to quantify damage in Rheumatoid Arthritis (RA) in clinical trials. However,…
Paired Diffusion: Generation of related, synthetic PET-CT-Segmentation scans using Linked Denoising Diffusion Probabilistic Models
Rowan Bradbury, Katherine A. Vallis, Bartlomiej W. Papiez
The rapid advancement of Artificial Intelligence (AI) in biomedical imaging and radiotherapy is hindered by the limited availability of large imaging data repositories. With recent…
Spinal Osteophyte Detection via Robust Patch Extraction on minimally annotated X-rays
Soumya Snigdha Kundu, Yuanhan Mo, Nicharee Srikijkasemwat +1
The development and progression of arthritis is strongly associated with osteophytes, which are small and elusive bone growths. This paper presents one of the first efforts towards…
Prediction of recurrence free survival of head and neck cancer using PET/CT radiomics and clinical information
Mona Furukawa, Daniel R. McGowan, Bartłomiej W. Papież
The 5-year survival rate of Head and Neck Cancer (HNC) has not improved over the past decade and one common cause of treatment failure is recurrence. In this paper, we built Cox pr…
Multi-Task Cooperative Learning via Searching for Flat Minima
Fuping Wu, Le Zhang, Yang Sun +3
Multi-task learning (MTL) has shown great potential in medical image analysis, improving the generalizability of the learned features and the performance in individual tasks. Howev…
VertXNet: An Ensemble Method for Vertebrae Segmentation and Identification of Spinal X-Ray
Yao Chen, Yuanhan Mo, Aimee Readie +5
Reliable vertebrae annotations are key to perform analysis of spinal X-ray images. However, obtaining annotation of vertebrae from those images is usually carried out manually due…
VertXNet: Automatic Segmentation and Identification of Lumbar and Cervical Vertebrae from Spinal X-ray Images
Yao Chen, Yuanhan Mo, Aimee Readie +3
Manual annotation of vertebrae on spinal X-ray imaging is costly and time-consuming due to bone shape complexity and image quality variations. In this study, we address this challe…
Explaining Chest X-ray Pathologies in Natural Language
Maxime Kayser, Cornelius Emde, Oana-Maria Camburu +3
Most deep learning algorithms lack explanations for their predictions, which limits their deployment in clinical practice. Approaches to improve explainability, especially in medic…
Recursive Deformable Image Registration Network with Mutual Attention
Jian-Qing Zheng, Ziyang Wang, Baoru Huang +3
Deformable image registration, estimating the spatial transformation between different images, is an important task in medical imaging. Many previous studies have used learning-bas…
Residual Aligner Network
Jian-Qing Zheng, Ziyang Wang, Baoru Huang +2
Image registration is important for medical imaging, the estimation of the spatial transformation between different images. Many previous studies have used learning-based methods f…
D-Net: Siamese based Network with Mutual Attention for Volume Alignment
Jian-Qing Zheng, Ngee Han Lim, Bartlomiej W. Papiez
Alignment of contrast and non-contrast-enhanced imaging is essential for the quantification of changes in several biomedical applications. In particular, the extraction of cartilag…
Automated fetal brain extraction from clinical Ultrasound volumes using 3D Convolutional Neural Networks
Felipe Moser, Ruobing Huang, Aris T. Papageorghiou +2
To improve the performance of most neuroimiage analysis pipelines, brain extraction is used as a fundamental first step in the image processing. But in the case of fetal brain deve…
Pieces-of-parts for supervoxel segmentation with global context: Application to DCE-MRI tumour delineation
Benjamin Irving, James M Franklin, Bartlomiej W Papiez +5
Rectal tumour segmentation in dynamic contrast-enhanced MRI (DCE-MRI) is a challenging task, and an automated and consistent method would be highly desirable to improve the modelli…