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Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data: 4th International Workshop, iMIMIC 2021, and 1st International Workshop, TDA4MedicalData 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings 1st ed. 2021 [Pehme köide]

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  • Formaat: Paperback / softback, 129 pages, kõrgus x laius: 235x155 mm, kaal: 226 g, 3 Illustrations, black and white; X, 129 p. 3 illus., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 12929
  • Ilmumisaeg: 21-Sep-2021
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030874435
  • ISBN-13: 9783030874438
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Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data: 4th International Workshop, iMIMIC 2021, and 1st International Workshop, TDA4MedicalData 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings 1st ed. 2021Suurem pilt
  • Formaat: Paperback / softback, 129 pages, kõrgus x laius: 235x155 mm, kaal: 226 g, 3 Illustrations, black and white; X, 129 p. 3 illus., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 12929
  • Ilmumisaeg: 21-Sep-2021
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030874435
  • ISBN-13: 9783030874438
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783030874438.html
Märksõnad:
This book constitutes the refereed joint proceedings of the 4th International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2020, and the First International Workshop on Topological Data Analysis and Its Applications for Medical Data, TDA4MedicalData 2021, held on September 27, 2021, in conjunction with the 24th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2021.

The 7 full papers presented at iMIMIC 2021 and 5 full papers held at TDA4MedicalData 2021 were carefully reviewed and selected from 12 submissions each. 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. TDA4MedicalData is focusing on using TDA techniques to enhance the performance, generalizability, efficiency, and explainability of the current methods applied to medical data.

iMIMIC 2021 Workshop.- Interpretable Deep Learning for Surgical Tool
Management.- Soft Attention Improves Skin Cancer Classification Performance.-
Deep Gradient based on Collective Arti cial Intelligence for AD Diagnosis and
Prognosis.- This explains That: Congruent Image-Report Generation for
Explainable Medical Image Analysis with Cyclic Generative Adversarial
Networks.- Visual Explanation by Unifying Adversarial Generation and Feature
Importance Attributions.- The Effect of the Loss on Generalization: Empirical
Study on Synthetic Lung Nodule Data.- Voxel-level Importance Maps for
Interpretable Brain Age Estimation.- TDA4MedicalData Workshop.- Lattice Paths
for Persistent Diagrams.- Neighborhood complex based machine learning (NCML)
models for drug design.- Predictive modelling of highly multiplexed tumour
tissue images by graph neural networks.- Statistical modeling of pulmonary
vasculatures with topological priors in CT volumes.- Topological Detection of
Alzheimer's Disease using Betti Curves.