Zobrazeno 1 - 10
of 113
pro vyhledávání: '"Tarroni, Giacomo"'
Federated learning is a decentralized collaborative training paradigm that preserves stakeholders' data ownership while improving performance and generalization. However, statistical heterogeneity among client datasets poses a fundamental challenge b
Externí odkaz:
http://arxiv.org/abs/2410.02006
Autor:
Marimont, Sergio Naval, Siomos, Vasilis, Baugh, Matthew, Tzelepis, Christos, Kainz, Bernhard, Tarroni, Giacomo
Unsupervised Anomaly Detection (UAD) methods aim to identify anomalies in test samples comparing them with a normative distribution learned from a dataset known to be anomaly-free. Approaches based on generative models offer interpretability by gener
Externí odkaz:
http://arxiv.org/abs/2407.06635
Autor:
Li, Maolin, Tarroni, Giacomo
In the field of medical image analysis, deep learning models have demonstrated remarkable success in enhancing diagnostic accuracy and efficiency. However, the reliability of these models is heavily dependent on the quality of training data, and the
Externí odkaz:
http://arxiv.org/abs/2312.15233
Autor:
Marimont, Sergio Naval, Baugh, Matthew, Siomos, Vasilis, Tzelepis, Christos, Kainz, Bernhard, Tarroni, Giacomo
Unsupervised Anomaly Detection (UAD) techniques aim to identify and localize anomalies without relying on annotations, only leveraging a model trained on a dataset known to be free of anomalies. Diffusion models learn to modify inputs $x$ to increase
Externí odkaz:
http://arxiv.org/abs/2311.15453
Federated Learning (FL) is a collaborative training paradigm that allows for privacy-preserving learning of cross-institutional models by eliminating the exchange of sensitive data and instead relying on the exchange of model parameters between the c
Externí odkaz:
http://arxiv.org/abs/2311.14625
The detection and localization of anomalies is one important medical image analysis task. Most commonly, Computer Vision anomaly detection approaches rely on manual annotations that are both time consuming and expensive to obtain. Unsupervised anomal
Externí odkaz:
http://arxiv.org/abs/2308.01412
Unsupervised Out-of-Distribution (OOD) detection consists in identifying anomalous regions in images leveraging only models trained on images of healthy anatomy. An established approach is to tokenize images and model the distribution of tokens with
Externí odkaz:
http://arxiv.org/abs/2307.14701
NeSy4VRD is a multifaceted resource designed to support the development of neurosymbolic AI (NeSy) research. NeSy4VRD re-establishes public access to the images of the VRD dataset and couples them with an extensively revised, quality-improved version
Externí odkaz:
http://arxiv.org/abs/2305.13258
U-Net has been the go-to architecture for medical image segmentation tasks, however computational challenges arise when extending the U-Net architecture to 3D images. We propose the Implicit U-Net architecture that adapts the efficient Implicit Repre
Externí odkaz:
http://arxiv.org/abs/2206.15217
Autor:
Tarroni, Giacomo <1983>
Myocardial perfusion quantification by means of Contrast-Enhanced Cardiac Magnetic Resonance images relies on time consuming frame-by-frame manual tracing of regions of interest. In this Thesis, a novel automated technique for myocardial segmentation
Externí odkaz:
http://amsdottorato.unibo.it/4850/