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

Interpretable and Annotation-Efficient Learning for Medical Image Computing

Szerző: 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
Nyelv: Angol
Kötés: Puha kötésű
Kiadó: Springer Nature Switzerland AG
Elérhetőség: Beszállítói készleten
Küldés 5-8 napon belül
19 201 Ft
This book constitutes the refereed joint proceedings of the Third International Workshop on Interpre...

Információk a könyvről

Szerző
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
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2020
oldal
292
EAN
9783030611651
ISBN
3030611655
Enbook ID
33266137
Kiadó
Springer Nature Switzerland AG
Súly
486
Méretek
155 x 235 x 18

Teljes leírás

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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