Výsledky vyhledávání - "Pachetti, Eva"

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Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification

Autor: Pachetti, Eva, Tsaftaris, Sotirios A., Colantonio, Sara

Background and objective: Employing deep learning models in critical domains such as medical imaging poses challenges associated with the limited availability of training data. We present a strategy for improving the performance and generalization ca

Externí odkaz: http://arxiv.org/abs/2403.17530

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Cine cardiac MRI reconstruction using a convolutional recurrent network with refinement

Autor: Xue, Yuyang, Du, Yuning, Carloni, Gianluca, Pachetti, Eva, Jordan, Connor, Tsaftaris, Sotirios A.

Cine Magnetic Resonance Imaging (MRI) allows for understanding of the heart's function and condition in a non-invasive manner. Undersampling of the $k$-space is employed to reduce the scan duration, thus increasing patient comfort and reducing the ri

Externí odkaz: http://arxiv.org/abs/2309.13385

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A Systematic Review of Few-Shot Learning in Medical Imaging

Autor: Pachetti, Eva, Colantonio, Sara

The lack of annotated medical images limits the performance of deep learning models, which usually need large-scale labelled datasets. Few-shot learning techniques can reduce data scarcity issues and enhance medical image analysis, especially with me

Externí odkaz: http://arxiv.org/abs/2309.11433

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Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI

Autor: Carloni, Gianluca, Pachetti, Eva, Colantonio, Sara

In this paper, we present a novel method to automatically classify medical images that learns and leverages weak causal signals in the image. Our framework consists of a convolutional neural network backbone and a causality-extractor module that extr

Externí odkaz: http://arxiv.org/abs/2309.10725

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