469 citations · 1.6k across the 93 of their papers we have counts for
13 papers · 1 filter
Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation
Jinghan Sun, Dong Wei, Zhe Xu +7
Anatomical abnormality detection and report generation of chest X-ray (CXR) are two essential tasks in clinical practice. The former aims at localizing and characterizing cardiopul…
Capacity Control is an Effective Memorization Mitigation Mechanism in Text-Conditional Diffusion Models
Raman Dutt, Pedro Sanchez, Ondrej Bohdal +2
In this work, we present compelling evidence that controlling model capacity during fine-tuning can effectively mitigate memorization in diffusion models. Specifically, we demonstr…
BMFT: Achieving Fairness via Bias-based Weight Masking Fine-tuning
Yuyang Xue, Junyu Yan, Raman Dutt +4
Developing models with robust group fairness properties is paramount, particularly in ethically sensitive domains such as medical diagnosis. Recent approaches to achieving fairness…
Surgical Task Automation Using Actor-Critic Frameworks and Self-Supervised Imitation Learning
Jingshuai Liu, Alain Andres, Yonghang Jiang +3
Surgical robot task automation has recently attracted great attention due to its potential to benefit both surgeons and patients. Reinforcement learning (RL) based approaches have…
CROCODILE: Causality aids RObustness via COntrastive DIsentangled LEarning
Gianluca Carloni, Sotirios A Tsaftaris, Sara Colantonio
Due to domain shift, deep learning image classifiers perform poorly when applied to a domain different from the training one. For instance, a classifier trained on chest X-ray (CXR…
The MRI Scanner as a Diagnostic: Image-less Active Sampling
Yuning Du, Rohan Dharmakumar, Sotirios A. Tsaftaris
Despite the high diagnostic accuracy of Magnetic Resonance Imaging (MRI), using MRI as a Point-of-Care (POC) disease identification tool poses significant accessibility challenges…
Erase to Enhance: Data-Efficient Machine Unlearning in MRI Reconstruction
Yuyang Xue, Jingshuai Liu, Steven McDonagh +1
Machine unlearning is a promising paradigm for removing unwanted data samples from a trained model, towards ensuring compliance with privacy regulations and limiting harmful biases…
GMT: Guided Mask Transformer for Leaf Instance Segmentation
Feng Chen, Sotirios A. Tsaftaris, Mario Valerio Giuffrida
Leaf instance segmentation is a challenging multi-instance segmentation task, aiming to separate and delineate each leaf in an image of a plant. Accurate segmentation of each leaf…
MemControl: Mitigating Memorization in Diffusion Models via Automated Parameter Selection
Raman Dutt, Ondrej Bohdal, Pedro Sanchez +2
Diffusion models excel in generating images that closely resemble their training data but are also susceptible to data memorization, raising privacy, ethical, and legal concerns, p…
Zero-Shot Medical Phrase Grounding with Off-the-shelf Diffusion Models
Konstantinos Vilouras, Pedro Sanchez, Alison Q. O'Neil +1
Localizing the exact pathological regions in a given medical scan is an important imaging problem that traditionally requires a large amount of bounding box ground truth annotation…
Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification
Eva Pachetti, Sotirios A. Tsaftaris, Sara Colantonio
Background and objective: Employing deep learning models in critical domains such as medical imaging poses challenges associated with the limited availability of training data. We…
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…
Inference Stage Denoising for Undersampled MRI Reconstruction
Yuyang Xue, Chen Qin, Sotirios A. Tsaftaris
Reconstruction of magnetic resonance imaging (MRI) data has been positively affected by deep learning. A key challenge remains: to improve generalisation to distribution shifts bet…