Search Results for author: Sara Colantonio

Found 11 papers, 1 papers with code

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

no code implementations26 Mar 2024 Eva Pachetti, Sotirios A. Tsaftaris, Sara Colantonio

Background and objective: Employing deep learning models in critical domains such as medical imaging poses challenges associated with the limited availability of training data.

Few-Shot Learning Image Classification +2

In-Bed Pose Estimation: A Review

no code implementations1 Feb 2024 Ziya Ata Yazıcı, Sara Colantonio, Hazim Kemal Ekenel

Human pose estimation, the process of identifying joint positions in a person's body from images or videos, represents a widely utilized technology across diverse fields, including healthcare.

Pose Estimation

A Systematic Review of Few-Shot Learning in Medical Imaging

no code implementations20 Sep 2023 Eva Pachetti, Sara Colantonio

This systematic review gives a comprehensive overview of few-shot learning in medical imaging.

Few-Shot Learning Image Registration

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

no code implementations19 Sep 2023 Gianluca Carloni, Eva Pachetti, Sara Colantonio

In this paper, we present a novel method to automatically classify medical images that learns and leverages weak causal signals in the image.

Decision Making Image Classification +4

Exploiting Causality Signals in Medical Images: A Pilot Study with Empirical Results

1 code implementation19 Sep 2023 Gianluca Carloni, Sara Colantonio

Our method consists of a convolutional neural network backbone and a causality-factors extractor module, which computes weights to enhance each feature map according to its causal influence in the scene.

Few-Shot Learning

The role of causality in explainable artificial intelligence

no code implementations18 Sep 2023 Gianluca Carloni, Andrea Berti, Sara Colantonio

More precisely, we seek to uncover what kinds of relationships exist between the two concepts and how one can benefit from them, for instance, in building trust in AI systems.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI)

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

no code implementations11 Aug 2023 Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah, Alejandro F Frangi, Alena Buyx, Anais Emelie, Andrea Lara, Antonio R Porras, An-Wen Chan, Arcadi Navarro, Ben Glocker, Benard O Botwe, Bishesh Khanal, Brigit Beger, Carol C Wu, Celia Cintas, Curtis P Langlotz, Daniel Rueckert, Deogratias Mzurikwao, Dimitrios I Fotiadis, Doszhan Zhussupov, Enzo Ferrante, Erik Meijering, Eva Weicken, Fabio A González, Folkert W Asselbergs, Fred Prior, Gabriel P Krestin, Gary Collins, Geletaw S Tegenaw, Georgios Kaissis, Gianluca Misuraca, Gianna Tsakou, Girish Dwivedi, Haridimos Kondylakis, Harsha Jayakody, Henry C Woodruf, Hugo JWL Aerts, Ian Walsh, Ioanna Chouvarda, Irène Buvat, Islem Rekik, James Duncan, Jayashree Kalpathy-Cramer, Jihad Zahir, Jinah Park, John Mongan, Judy W Gichoya, Julia A Schnabel, Kaisar Kushibar, Katrine Riklund, Kensaku MORI, Kostas Marias, Lameck M Amugongo, Lauren A Fromont, Lena Maier-Hein, Leonor Cerdá Alberich, Leticia Rittner, Lighton Phiri, Linda Marrakchi-Kacem, Lluís Donoso-Bach, Luis Martí-Bonmatí, M Jorge Cardoso, Maciej Bobowicz, Mahsa Shabani, Manolis Tsiknakis, Maria A Zuluaga, Maria Bielikova, Marie-Christine Fritzsche, Marius George Linguraru, Markus Wenzel, Marleen de Bruijne, Martin G Tolsgaard, Marzyeh Ghassemi, Md Ashrafuzzaman, Melanie Goisauf, Mohammad Yaqub, Mohammed Ammar, Mónica Cano Abadía, Mukhtar M E Mahmoud, Mustafa Elattar, Nicola Rieke, Nikolaos Papanikolaou, Noussair Lazrak, Oliver Díaz, Olivier Salvado, Oriol Pujol, Ousmane Sall, Pamela Guevara, Peter Gordebeke, Philippe Lambin, Pieta Brown, Purang Abolmaesumi, Qi Dou, Qinghua Lu, Richard Osuala, Rose Nakasi, S Kevin Zhou, Sandy Napel, Sara Colantonio, Shadi Albarqouni, Smriti Joshi, Stacy Carter, Stefan Klein, Steffen E Petersen, Susanna Aussó, Suyash Awate, Tammy Riklin Raviv, Tessa Cook, Tinashe E M Mutsvangwa, Wendy A Rogers, Wiro J Niessen, Xènia Puig-Bosch, Yi Zeng, Yunusa G Mohammed, Yves Saint James Aquino, Zohaib Salahuddin, Martijn P A Starmans

This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.

Fairness

Reproducibility of Machine Learning: Terminology, Recommendations and Open Issues

no code implementations24 Feb 2023 Riccardo Albertoni, Sara Colantonio, Piotr Skrzypczyński, Jerzy Stefanowski

Reproducibility is one of the core dimensions that concur to deliver Trustworthy Artificial Intelligence.

State of the Art of Audio- and Video-Based Solutions for AAL

no code implementations26 Jun 2022 Slavisa Aleksic, Michael Atanasov, Jean Calleja Agius, Kenneth Camilleri, Anto Cartolovni, Pau Climent-Peerez, Sara Colantonio, Stefania Cristina, Vladimir Despotovic, Hazim Kemal Ekenel, Ekrem Erakin, Francisco Florez-Revuelta, Danila Germanese, Nicole Grech, Steinunn Gróa Sigurðardóttir, Murat Emirzeoglu, Ivo Iliev, Mladjan Jovanovic, Martin Kampel, William Kearns, Andrzej Klimczuk, Lambros Lambrinos, Jennifer Lumetzberger, Wiktor Mucha, Sophie Noiret, Zada Pajalic, Rodrigo Rodriguez Peerez, Galidiya Petrova, Sintija Petrovica, Peter Pocta, Angelica Poli, Mara Pudane, Susanna Spinsante, Albert Ali Salah, Maria Jose Santofimia, Anna Sigridur Islind, Lacramioara Stoicu-Tivadar, Hilda Tellioglu, Andrej Zgank

The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely (i) lifelogging and self-monitoring, (ii) remote monitoring of vital signs, (iii) emotional state recognition, (iv) food intake monitoring, activity and behaviour recognition, (v) activity and personal assistance, (vi) gesture recognition, (vii) fall detection and prevention, (viii) mobility assessment and frailty recognition, and (ix) cognitive and motor rehabilitation.

Gesture Recognition

FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging

no code implementations20 Sep 2021 Karim Lekadir, Richard Osuala, Catherine Gallin, Noussair Lazrak, Kaisar Kushibar, Gianna Tsakou, Susanna Aussó, Leonor Cerdá Alberich, Kostas Marias, Manolis Tsiknakis, Sara Colantonio, Nickolas Papanikolaou, Zohaib Salahuddin, Henry C Woodruff, Philippe Lambin, Luis Martí-Bonmatí

The recent advancements in artificial intelligence (AI) combined with the extensive amount of data generated by today's clinical systems, has led to the development of imaging AI solutions across the whole value chain of medical imaging, including image reconstruction, medical image segmentation, image-based diagnosis and treatment planning.

Fairness Image Reconstruction +3

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