Zobrazeno 1 - 10
of 166
pro vyhledávání: '"Lundström, Claes"'
Multiple-instance learning (MIL) is an attractive approach for digital pathology applications as it reduces the costs related to data collection and labelling. However, it is not clear how sensitive MIL is to clinically realistic domain shifts, i.e.,
Externí odkaz:
http://arxiv.org/abs/2405.09934
The assessment of image denoising results depends on the respective application area, i.e. image compression, still-image acquisition, and medical images require entirely different behavior of the applied denoising method. In this paper we propose a
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-89674
Deep learning (DL) has shown great potential in digital pathology applications. The robustness of a diagnostic DL-based solution is essential for safe clinical deployment. In this work we evaluate if adding uncertainty estimates for DL predictions in
Externí odkaz:
http://arxiv.org/abs/2112.09693
Unsupervised learning has made substantial progress over the last few years, especially by means of contrastive self-supervised learning. The dominating dataset for benchmarking self-supervised learning has been ImageNet, for which recent methods are
Externí odkaz:
http://arxiv.org/abs/2112.05760
Insufficient training data is a major bottleneck for most deep learning practices, not least in medical imaging where data is difficult to collect and publicly available datasets are scarce due to ethics and privacy. This work investigates the use of
Externí odkaz:
http://arxiv.org/abs/2104.11797
Machine learning (ML) algorithms are optimized for the distribution represented by the training data. For outlier data, they often deliver predictions with equal confidence, even though these should not be trusted. In order to deploy ML-based digital
Externí odkaz:
http://arxiv.org/abs/2103.08945
Artificial intelligence (AI) has shown great promise for diagnostic imaging assessments. However, the application of AI to support medical diagnostics in clinical routine comes with many challenges. The algorithms should have high prediction accuracy
Externí odkaz:
http://arxiv.org/abs/2008.06353
Domain shift is a significant problem in histopathology. There can be large differences in data characteristics of whole-slide images between medical centers and scanners, making generalization of deep learning to unseen data difficult. To gain a bet
Externí odkaz:
http://arxiv.org/abs/1909.11575
Digital whole-slide images of pathological tissue samples have recently become feasible for use within routine diagnostic practice. These gigapixel sized images enable pathologists to perform reviews using computer workstations instead of microscopes
Externí odkaz:
http://arxiv.org/abs/1610.04141
Autor:
Lundström, Claes
Direct Volume Rendering (DVR) is a visualization technique that has proved to be a very powerful tool in many scientific visualization applications. Diagnostic medical imaging is one domain where DVR could provide clear benefits in terms of unprecede
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-9561