200 citations · 485 across the 22 of their papers we have counts for
23 papers
From Training to Deployment: Post-Hoc Causal Feature Identification via Sensitivity Ratios
Athanasios Vlontzos, Giorgos Papanastasiou, Bernhard Kainz +1
Given a model that is already trained, which features does it rely on causally versus spuriously? Existing methods require access to the training procedure and cannot answer this p…
Entangled by Design: Spurious Intra-Variable Signal Routing in Tabular In-Context Learners
Athanasios Vlontzos, Giorgos Papanastasiou, Bernhard Kainz +1
Consider a model trained at a single hospital to predict patient recovery, where the measured feature bundles the patient's true health signal () with a systematic artefact…
Causally Steered Diffusion for Automated Video Counterfactual Generation
Nikos Spyrou, Athanasios Vlontzos, Paraskevas Pegios +5
Adapting text-to-image (T2I) latent diffusion models (LDMs) to video editing has shown strong visual fidelity and controllability, but challenges remain in maintaining causal relat…
A Novel Coronary Artery Registration Method Based on Super-pixel Particle Swarm Optimization
Peng Qi, Wenxi Qu, Tianliang Yao +5
Percutaneous Coronary Intervention (PCI) is a minimally invasive procedure that improves coronary blood flow and treats coronary artery disease. Although PCI typically requires 2D…
Reason Like a Radiologist: Chain-of-Thought and Reinforcement Learning for Verifiable Report Generation
Peiyuan Jing, Kinhei Lee, Zhenxuan Zhang +7
Radiology report generation is critical for efficiency but current models lack the structured reasoning of experts, hindering clinical trust and explainability by failing to link v…
Semi-Supervised Medical Image Segmentation via Knowledge Mining from Large Models
Yuchen Mao, Hongwei Li, Yinyi Lai +4
Large-scale vision models like SAM have extensive visual knowledge, yet their general nature and computational demands limit their use in specialized tasks like medical image segme…
Anatomy-Guided Radiology Report Generation with Pathology-Aware Regional Prompts
Yijian Gao, Dominic Marshall, Xiaodan Xing +4
Radiology reporting generative AI holds significant potential to alleviate clinical workloads and streamline medical care. However, achieving high clinical accuracy is challenging,…
SeLoRA: Self-Expanding Low-Rank Adaptation of Latent Diffusion Model for Medical Image Synthesis
Yuchen Mao, Hongwei Li, Wei Pang +3
The persistent challenge of medical image synthesis posed by the scarcity of annotated data and the need to synthesize `missing modalities' for multi-modal analysis, underscored th…
Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review
Anshu Ankolekar, Sebastian Boie, Maryam Abdollahyan +11
Federated Learning (FL) has emerged as a promising solution to address the limitations of centralised machine learning (ML) in oncology, particularly in overcoming privacy concerns…
Artificial Immunofluorescence in a Flash: Rapid Synthetic Imaging from Brightfield Through Residual Diffusion
Xiaodan Xing, Chunling Tang, Siofra Murdoch +10
Immunofluorescent (IF) imaging is crucial for visualizing biomarker expressions, cell morphology and assessing the effects of drug treatments on sub-cellular components. IF imaging…
Benchmarking Counterfactual Image Generation
Thomas Melistas, Nikos Spyrou, Nefeli Gkouti +5
Generative AI has revolutionised visual content editing, empowering users to effortlessly modify images and videos. However, not all edits are equal. To perform realistic edits in…
From Explainable to Interpretable Deep Learning for Natural Language Processing in Healthcare: How Far from Reality?
Guangming Huang, Yingya Li, Shoaib Jameel +2
Deep learning (DL) has substantially enhanced natural language processing (NLP) in healthcare research. However, the increasing complexity of DL-based NLP necessitates transparent…
Explainable unsupervised multi-modal image registration using deep networks
Chengjia Wang, Giorgos Papanastasiou
Clinical decision making from magnetic resonance imaging (MRI) combines complementary information from multiple MRI sequences (defined as 'modalities'). MRI image registration aims…
Is attention all you need in medical image analysis? A review
Giorgos Papanastasiou, Nikolaos Dikaios, Jiahao Huang +2
Medical imaging is a key component in clinical diagnosis, treatment planning and clinical trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance…
You Don't Have to Be Perfect to Be Amazing: Unveil the Utility of Synthetic Images
Xiaodan Xing, Federico Felder, Yang Nan +3
Synthetic images generated from deep generative models have the potential to address data scarcity and data privacy issues. The selection of synthesis models is mostly based on ima…
Less is More: Unsupervised Mask-guided Annotated CT Image Synthesis with Minimum Manual Segmentations
Xiaodan Xing, Giorgos Papanastasiou, Simon Walsh +1
As a pragmatic data augmentation tool, data synthesis has generally returned dividends in performance for deep learning based medical image analysis. However, generating correspond…
Unsupervised Image Registration Towards Enhancing Performance and Explainability in Cardiac And Brain Image Analysis
Chengjia Wang, Guang Yang, Giorgos Papanastasiou
Magnetic Resonance Imaging (MRI) typically recruits multiple sequences (defined here as "modalities"). As each modality is designed to offer different anatomical and functional cli…
Semi-supervised Pathology Segmentation with Disentangled Representations
Haochuan Jiang, Agisilaos Chartsias, Xinheng Zhang +6
Automated pathology segmentation remains a valuable diagnostic tool in clinical practice. However, collecting training data is challenging. Semi-supervised approaches by combining…
Disentangle, align and fuse for multimodal and semi-supervised image segmentation
Agisilaos Chartsias, Giorgos Papanastasiou, Chengjia Wang +4
Magnetic resonance (MR) protocols rely on several sequences to assess pathology and organ status properly. Despite advances in image analysis, we tend to treat each sequence, here…
FIRE: Unsupervised bi-directional inter-modality registration using deep networks
Chengjia Wang, Giorgos Papanastasiou, Agisilaos Chartsias +3
Inter-modality image registration is an critical preprocessing step for many applications within the routine clinical pathway. This paper presents an unsupervised deep inter-modali…
Disentangled Representation Learning in Cardiac Image Analysis
Agisilaos Chartsias, Thomas Joyce, Giorgos Papanastasiou +4
Typically, a medical image offers spatial information on the anatomy (and pathology) modulated by imaging specific characteristics. Many imaging modalities including Magnetic Reson…
Unsupervised learning for cross-domain medical image synthesis using deformation invariant cycle consistency networks
Chengjia Wang, Gillian Macnaught, Giorgos Papanastasiou +2
Recently, the cycle-consistent generative adversarial networks (CycleGAN) has been widely used for synthesis of multi-domain medical images. The domain-specific nonlinear deformati…
Factorised spatial representation learning: application in semi-supervised myocardial segmentation
Agisilaos Chartsias, Thomas Joyce, Giorgos Papanastasiou +5
The success and generalisation of deep learning algorithms heavily depend on learning good feature representations. In medical imaging this entails representing anatomical informat…