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Carte Interpretable and Annotation-Efficient Learning for Medical Image Computing Badri Roysam

Interpretable and Annotation-Efficient Learning for Medical Image Computing

Autor: Badri Roysam, Kevin Zhou, Steve Jiang, Ngan Le, Khoa Luu, Raphael Sznitman, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, Hien van Nguyen, Samaneh Abbasi, Nicholas Heller, Pedro Henriques Abreu
Limbă: engleză
Legare: Carte broșată
Editura: Springer Nature Switzerland AG
Disponibilitate: În depozitul extern
Expediem în 5-8 zile
277.38 lei
This book constitutes the refereed joint proceedings of the Third International Workshop on Interpre...

Informații despre carte

Autor
Badri Roysam, Kevin Zhou, Steve Jiang, Ngan Le, Khoa Luu, Raphael Sznitman, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, Hien van Nguyen, Samaneh Abbasi, Nicholas Heller, Pedro Henriques Abreu
Limbă
engleză
Legare
Carte - Carte broșată
Publicat
2020
Pagini
292
EAN
9783030611651
ISBN
3030611655
Enbook ID
33266137
Editura
Springer Nature Switzerland AG
Greutate
486
Dimensiuni
155 x 235 x 18

Descriere completă

This book constitutes the refereed joint proceedings of the Third International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2020, the Second International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2020, and the 5th International Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis, LABELS 2020, held in conjunction with the 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020.The 8 full papers presented at iMIMIC 2020, 11 full papers to MIL3ID 2020, and the 10 full papers presented at LABELS 2020 were carefully reviewed and selected from 16 submissions to iMIMIC, 28 to MIL3ID, and 12 submissions to LABELS. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. MIL3ID deals with best practices in medical image learning with label scarcity and data imperfection. The LABELS papers present a variety of approaches for dealing with a limited number of labels, from semi-supervised learning to crowdsourcing.

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