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of 140
pro vyhledávání: '"Deligianni., Fani"'
Semi-supervised medical image segmentation has shown promise in training models with limited labeled data and abundant unlabeled data. However, state-of-the-art methods ignore a potentially valuable source of unsupervised semantic information -- spat
Externí odkaz:
http://arxiv.org/abs/2409.10422
Autor:
Tragakis, Athanasios, Liu, Qianying, Kaul, Chaitanya, Roy, Swalpa Kumar, Dai, Hang, Deligianni, Fani, Murray-Smith, Roderick, Faccio, Daniele
Publikováno v:
2024 IEEE International Symposium on Biomedical Imaging (ISBI)
We propose a novel transformer-style architecture called Global-Local Filter Network (GLFNet) for medical image segmentation and demonstrate its state-of-the-art performance. We replace the self-attention mechanism with a combination of global-local
Externí odkaz:
http://arxiv.org/abs/2403.00396
Congenital heart disease (CHD) is a relatively rare disease that affects patients at birth and results in extremely heterogeneous anatomical and functional defects. 12-lead ECG signal is routinely collected in CHD patients because it provides signifi
Externí odkaz:
http://arxiv.org/abs/2312.09437
Radiology reports are detailed text descriptions of the content of medical scans. Each report describes the presence/absence and location of relevant clinical findings, commonly including comparison with prior exams of the same patient to describe ho
Externí odkaz:
http://arxiv.org/abs/2310.05881
Worldwide, many millions of people die suddenly and unexpectedly each year, either with or without a prior history of cardiovascular disease. Such events are sparse (once in a lifetime), many victims will not have had prior investigations for cardiac
Externí odkaz:
http://arxiv.org/abs/2308.16067
The task of radiology reporting comprises describing and interpreting the medical findings in radiographic images, including description of their location and appearance. Automated approaches to radiology reporting require the image to be encoded int
Externí odkaz:
http://arxiv.org/abs/2308.15961
Publikováno v:
BMVC 2023
Semi-supervised learning has demonstrated great potential in medical image segmentation by utilizing knowledge from unlabeled data. However, most existing approaches do not explicitly capture high-level semantic relations between distant regions, whi
Externí odkaz:
http://arxiv.org/abs/2306.14293
Music therapy has emerged recently as a successful intervention that improves patient's outcome in a large range of neurological and mood disorders without adverse effects. Brain networks are entrained to music in ways that can be explained both via
Externí odkaz:
http://arxiv.org/abs/2305.14364
The fight or flight phenomena is of evolutionary origin and responsible for the type of defensive behaviours enacted, when in the face of threat. This review attempts to draw the link between fear and aggression as behavioural motivations for fight o
Externí odkaz:
http://arxiv.org/abs/2305.00038
Autor:
Liu, Qianying, Kaul, Chaitanya, Wang, Jun, Anagnostopoulos, Christos, Murray-Smith, Roderick, Deligianni, Fani
For medical image semantic segmentation (MISS), Vision Transformers have emerged as strong alternatives to convolutional neural networks thanks to their inherent ability to capture long-range correlations. However, existing research uses off-the-shel
Externí odkaz:
http://arxiv.org/abs/2210.08066