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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
- Author(s):
- Reyes, Mauricio, editor
- Henriques Abreu, Pedro, editor
- Cardoso, Jaime, editor
- Hajij, Mustafa, editor
- Zamzmi, Ghada, editor
- Rahul, Paul, editor
- Thakur, Lokendra, editor
- NLM Title Abbreviation:
- Interpret Mach Intell Med Image Comput Topogr Data Anal Appl Med Data (2021)
- Title(s):
- 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 / edited by Mauricio Reyes, Pedro Henriques Abreu, Jaime Cardoso, Mustafa Hajij, Ghada Zamzmi, Paul Rahul, Lokendra Thakur.
- Series:
- Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12929
- Edition:
- 1st ed. 2021.
- Country of Publication:
- Switzerland
- Publisher:
- Cham : Springer International Publishing : Imprint: Springer, 2021.
- Description:
- 1 online resource (X, 129 p. 3 illus. :) online resource.
-
Language:
- English
- ISBN:
- 9783030874445
3030874443
9783030874445
- Electronic Links:
- Access not provided by NLM
- Summary:
- 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.
- In:
- PubMed: Selected citations only
- Current Indexing Status:
- Not currently indexed for MEDLINE. Citations are for articles where the manuscript was deposited in PubMed Central (PMC) in compliance with public access policies. For further information, see Author Manuscripts in PMC.
- Notes:
- Also issued in print.
- Contents:
- 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. .
- Other ID:
- (OCoLC)1282249637
- Collection Status:
- Not in the NLM Collection
- NLM ID:
- 9918316788806676 [Electronic Resource]