469 citations · 1.6k across the 93 of their papers we have counts for
9 papers · 1 filter
Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resampling
Haochuan Jiang, Chengjia Wang, Agisilaos Chartsias +1
Automatic segmentation of multi-sequence (multi-modal) cardiac MR (CMR) images plays a significant role in diagnosis and management for a variety of cardiac diseases. However, the…
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…
Disentangled Representations for Domain-generalized Cardiac Segmentation
Xiao Liu, Spyridon Thermos, Agisilaos Chartsias +2
Robust cardiac image segmentation is still an open challenge due to the inability of the existing methods to achieve satisfactory performance on unseen data of different domains. S…
INSIDE: Steering Spatial Attention with Non-Imaging Information in CNNs
Grzegorz Jacenków, Alison Q. O'Neil, Brian Mohr +1
We consider the problem of integrating non-imaging information into segmentation networks to improve performance. Conditioning layers such as FiLM provide the means to selectively…
Measuring the Biases and Effectiveness of Content-Style Disentanglement
Xiao Liu, Spyridon Thermos, Gabriele Valvano +3
A recent spate of state-of-the-art semi- and un-supervised solutions disentangle and encode image "content" into a spatial tensor and image appearance or "style" into a vector, to…
Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates
Gabriele Valvano, Andrea Leo, Sotirios A. Tsaftaris
Large, fine-grained image segmentation datasets, annotated at pixel-level, are difficult to obtain, particularly in medical imaging, where annotations also require expert knowledge…
Pseudo-healthy synthesis with pathology disentanglement and adversarial learning
Tian Xia, Agisilaos Chartsias, Sotirios A. Tsaftaris
Pseudo-healthy synthesis is the task of creating a subject-specific `healthy' image from a pathological one. Such images can be helpful in tasks such as anomaly detection and under…
Have you forgotten? A method to assess if machine learning models have forgotten data
Xiao Liu, Sotirios A Tsaftaris
In the era of deep learning, aggregation of data from several sources is a common approach to ensuring data diversity. Let us consider a scenario where several providers contribute…
Teacher-Student chain for efficient semi-supervised histology image classification
Shayne Shaw, Maciej Pajak, Aneta Lisowska +2
Deep learning shows great potential for the domain of digital pathology. An automated digital pathology system could serve as a second reader, perform initial triage in large scree…