Zobrazeno 1 - 10
of 157
pro vyhledávání: '"Chartsias A"'
Autor:
Bransby, Kit M., Kim, Woo-jin Cho, Oliveira, Jorge, Thorley, Alex, Beqiri, Arian, Gomez, Alberto, Chartsias, Agisilaos
Building an echocardiography view classifier that maintains performance in real-life cases requires diverse multi-site data, and frequent updates with newly available data to mitigate model drift. Simply fine-tuning on new datasets results in "catast
Externí odkaz:
http://arxiv.org/abs/2407.21577
Autor:
Bransby, Kit Mills, Beqiri, Arian, Kim, Woo-Jin Cho, Oliveira, Jorge, Chartsias, Agisilaos, Gomez, Alberto
Neural networks can learn spurious correlations that lead to the correct prediction in a validation set, but generalise poorly because the predictions are right for the wrong reason. This undesired learning of naive shortcuts (Clever Hans effect) can
Externí odkaz:
http://arxiv.org/abs/2406.19148
Autor:
Gomez, Alberto, Porumb, Mihaela, Mumith, Angela, Judge, Thierry, Gao, Shan, Kim, Woo-Jin Cho, Oliveira, Jorge, Chartsias, Agis
We propose a new method to automatically contour the left ventricle on 2D echocardiographic images. Unlike most existing segmentation methods, which are based on predicting segmentation masks, we focus at predicting the endocardial contour and the ke
Externí odkaz:
http://arxiv.org/abs/2207.06330
Autor:
Judge, Thierry, Bernard, Olivier, Porumb, Mihaela, Chartsias, Agis, Beqiri, Arian, Jodoin, Pierre-Marc
Accurate uncertainty estimation is a critical need for the medical imaging community. A variety of methods have been proposed, all direct extensions of classification uncertainty estimations techniques. The independent pixel-wise uncertainty estimate
Externí odkaz:
http://arxiv.org/abs/2206.07664
Autor:
Chartsias, Agisilaos, Gao, Shan, Mumith, Angela, Oliveira, Jorge, Bhatia, Kanwal, Kainz, Bernhard, Beqiri, Arian
Analysis of cardiac ultrasound images is commonly performed in routine clinical practice for quantification of cardiac function. Its increasing automation frequently employs deep learning networks that are trained to predict disease or detect image f
Externí odkaz:
http://arxiv.org/abs/2108.03124
Publikováno v:
MICCAI-2020 MyoPS Challenge Paper
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 performance of relevant algorithms is significantly affected by the pr
Externí odkaz:
http://arxiv.org/abs/2009.02569
Autor:
Jiang, Haochuan, Chartsias, Agisilaos, Zhang, Xinheng, Papanastasiou, Giorgos, Semple, Scott, Dweck, Mark, Semple, David, Dharmakumar, Rohan, Tsaftaris, Sotirios A.
Publikováno v:
MICCAI-2020 DART workshop
Automated pathology segmentation remains a valuable diagnostic tool in clinical practice. However, collecting training data is challenging. Semi-supervised approaches by combining labelled and unlabelled data can offer a solution to data scarcity. An
Externí odkaz:
http://arxiv.org/abs/2009.02564
Autor:
Liu, Xiao, Thermos, Spyridon, Valvano, Gabriele, Chartsias, Agisilaos, O'Neil, Alison, Tsaftaris, Sotirios A.
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 achieve good performance in spatially equivariant tasks (e.g. image-to
Externí odkaz:
http://arxiv.org/abs/2008.12378
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. Since the acquisition and annotation of medical data are costly and tim
Externí odkaz:
http://arxiv.org/abs/2008.11514
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 understanding changes induced by pathology and disease. In this paper, we p
Externí odkaz:
http://arxiv.org/abs/2005.01607