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050 4 _aTA1634
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072 7 _aCOM016000
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245 1 0 _aInterpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data
_h[electronic resource] :
_b4th International Workshop, iMIMIC 2021, and 1st International Workshop, TDA4MedicalData 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings /
_cedited by Mauricio Reyes, Pedro Henriques Abreu, Jaime Cardoso, Mustafa Hajij, Ghada Zamzmi, Paul Rahul, Lokendra Thakur.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aX, 129 p. 3 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
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490 1 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
_x3004-9954 ;
_v12929
505 0 _aiMIMIC 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. .
520 _aThis 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.
650 0 _aImage processing
_xDigital techniques.
650 0 _aComputer vision.
650 0 _aComputer science.
650 0 _aBioinformatics.
650 0 _aMachine learning.
650 0 _aApplication software.
650 1 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
650 2 4 _aTheory of Computation.
650 2 4 _aComputational and Systems Biology.
650 2 4 _aMachine Learning.
650 2 4 _aComputer and Information Systems Applications.
700 1 _aReyes, Mauricio.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aHenriques Abreu, Pedro.
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700 1 _aCardoso, Jaime.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aHajij, Mustafa.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aZamzmi, Ghada.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aRahul, Paul.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aThakur, Lokendra.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030874438
776 0 8 _iPrinted edition:
_z9783030874452
830 0 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
_x3004-9954 ;
_v12929
856 4 0 _uhttps://doi.org/10.1007/978-3-030-87444-5
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