國立虎尾科技大學 |

Interpretable and Annotation-Efficient Learning for Medical Image Computing = Third International Workshop, iMIMIC 2020, Second International Workshop, MIL3ID 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Interpretable and Annotation-Efficient Learning for Medical Image Computing/ edited by Jaime Cardoso, Hien Van Nguyen, Nicholas Heller, Pedro Henriques Abreu, Ivana Isgum, Wilson Silva, Ricardo Cruz, Jose Pereira Amorim, Vishal Patel, Badri Roysam, Kevin Zhou, Steve Jiang, Ngan Le, Khoa Luu, Raphael Sznitman, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, Samaneh Abbasi.
其他題名:	Third International Workshop, iMIMIC 2020, Second International Workshop, MIL3ID 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings /
其他作者:	Cardoso, Jaime.
面頁冊數:	XVII, 292 p. 109 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Artificial intelligence. -
電子資源:	https://doi.org/10.1007/978-3-030-61166-8
ISBN:	9783030611668

Interpretable and Annotation-Efficient Learning for Medical Image Computing = Third International Workshop, iMIMIC 2020, Second International Workshop, MIL3ID 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings /
Interpretable and Annotation-Efficient Learning for Medical Image ComputingThird International Workshop, iMIMIC 2020, Second International Workshop, MIL3ID 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings /[electronic resource] :edited by Jaime Cardoso, Hien Van Nguyen, Nicholas Heller, Pedro Henriques Abreu, Ivana Isgum, Wilson Silva, Ricardo Cruz, Jose Pereira Amorim, Vishal Patel, Badri Roysam, Kevin Zhou, Steve Jiang, Ngan Le, Khoa Luu, Raphael Sznitman, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, Samaneh Abbasi. - 1st ed. 2020. - XVII, 292 p. 109 illus.online resource. - Image Processing, Computer Vision, Pattern Recognition, and Graphics ;12446. - Image Processing, Computer Vision, Pattern Recognition, and Graphics ;9219.

iMIMIC 2020 -- Assessing attribution maps for explaining CNN-based vertebral fracture classifiers -- Projective Latent Interventions for Understanding and Fine-tuning Classifiers -- Interpretable CNN Pruning for Preserving Scale-Covariant Features in Medical Imaging -- Improving the Performance and Explainability of Mammogram Classifiers with Local Annotations -- Improving Interpretability for Computer-aided Diagnosis tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-based Explanations -- Explainable Disease Classification via weakly-supervised segmentation -- Reliable Saliency Maps for Weakly-Supervised Localization of Disease Patterns -- Explainability for regression CNN in fetal head circumference estimation from ultrasound images -- MIL3ID 2020 -- Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteins -- Semi-supervised Instance Segmentation with a Learned Shape Prior -- COMe-SEE: Cross-Modality Semantic Embedding Ensemble for Generalized Zero-Shot Diagnosis of Chest Radiographs -- Semi-supervised Machine Learning with MixMatch and Equivalence Classes -- Non-contrast CT Liver Segmentation using CycleGAN Data Augmentation from Contrast Enhanced CT -- Uncertainty Estimation in Medical Image Localization: Towards Robust Anterior Thalamus Targeting for Deep Brain Stimulation -- A Case Study of Transfer of Lesion-Knowledge -- Transfer Learning With Joint Optimization for Label-Efficient Medical Image Anomaly Detection -- Unsupervised Wasserstein Distance Guided Domain Adaptation for 3D Multi-Domain Liver Segmentation -- HydraMix-Net: A Deep Multi-task Semi-supervised Learning Approach for Cell Detection and Classification -- Semi-supervised classification of chest radiographs -- LABELS 2020 -- Risk of training diagnostic algorithms on data with demographic bias -- Semi-Weakly Supervised Learning for Prostate Cancer Image Classification with Teacher-Student Deep Convolutional Networks -- Are pathologist-defined labels reproducible? Comparison of the TUPAC16 mitotic figure dataset with an alternative set of labels -- EasierPath: An Open-source Tool for Human-in-the-loop Deep Learning of Renal Pathology -- Imbalance-Effective Active Learning in Nucleus, Lymphocyte and Plasma Cell Detection -- Labeling of Multilingual Breast MRI Reports -- Predicting Scores of Medical Imaging Segmentation Methods with Meta-Learning -- Labelling imaging datasets on the basis of neuroradiology reports: a validation study -- Semi-Supervised Learning for Instrument Detection with a Class Imbalanced Dataset -- Paying Per-label Attention for Multi-label Extraction from Radiology Reports.

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.

ISBN: 9783030611668

Standard No.: 10.1007/978-3-030-61166-8doiSubjects--Topical Terms:

559380
Artificial intelligence.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

Interpretable and Annotation-Efficient Learning for Medical Image Computing = Third International Workshop, iMIMIC 2020, Second International Workshop, MIL3ID 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings /
LDR:05839nam a22004095i 4500 001 1030952
003 DE-He213
005 20201031082829.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030611668 $9 978-3-030-61166-8
024 7 $a 10.1007/978-3-030-61166-8 $2 doi
035 $a 978-3-030-61166-8
050 4 $a Q334-342
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
245 1 0 $a Interpretable and Annotation-Efficient Learning for Medical Image Computing $h [electronic resource] : $b Third International Workshop, iMIMIC 2020, Second International Workshop, MIL3ID 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings / $c edited by Jaime Cardoso, Hien Van Nguyen, Nicholas Heller, Pedro Henriques Abreu, Ivana Isgum, Wilson Silva, Ricardo Cruz, Jose Pereira Amorim, Vishal Patel, Badri Roysam, Kevin Zhou, Steve Jiang, Ngan Le, Khoa Luu, Raphael Sznitman, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, Samaneh Abbasi.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a XVII, 292 p. 109 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Image Processing, Computer Vision, Pattern Recognition, and Graphics ; $v 12446
505 0 $a iMIMIC 2020 -- Assessing attribution maps for explaining CNN-based vertebral fracture classifiers -- Projective Latent Interventions for Understanding and Fine-tuning Classifiers -- Interpretable CNN Pruning for Preserving Scale-Covariant Features in Medical Imaging -- Improving the Performance and Explainability of Mammogram Classifiers with Local Annotations -- Improving Interpretability for Computer-aided Diagnosis tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-based Explanations -- Explainable Disease Classification via weakly-supervised segmentation -- Reliable Saliency Maps for Weakly-Supervised Localization of Disease Patterns -- Explainability for regression CNN in fetal head circumference estimation from ultrasound images -- MIL3ID 2020 -- Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteins -- Semi-supervised Instance Segmentation with a Learned Shape Prior -- COMe-SEE: Cross-Modality Semantic Embedding Ensemble for Generalized Zero-Shot Diagnosis of Chest Radiographs -- Semi-supervised Machine Learning with MixMatch and Equivalence Classes -- Non-contrast CT Liver Segmentation using CycleGAN Data Augmentation from Contrast Enhanced CT -- Uncertainty Estimation in Medical Image Localization: Towards Robust Anterior Thalamus Targeting for Deep Brain Stimulation -- A Case Study of Transfer of Lesion-Knowledge -- Transfer Learning With Joint Optimization for Label-Efficient Medical Image Anomaly Detection -- Unsupervised Wasserstein Distance Guided Domain Adaptation for 3D Multi-Domain Liver Segmentation -- HydraMix-Net: A Deep Multi-task Semi-supervised Learning Approach for Cell Detection and Classification -- Semi-supervised classification of chest radiographs -- LABELS 2020 -- Risk of training diagnostic algorithms on data with demographic bias -- Semi-Weakly Supervised Learning for Prostate Cancer Image Classification with Teacher-Student Deep Convolutional Networks -- Are pathologist-defined labels reproducible? Comparison of the TUPAC16 mitotic figure dataset with an alternative set of labels -- EasierPath: An Open-source Tool for Human-in-the-loop Deep Learning of Renal Pathology -- Imbalance-Effective Active Learning in Nucleus, Lymphocyte and Plasma Cell Detection -- Labeling of Multilingual Breast MRI Reports -- Predicting Scores of Medical Imaging Segmentation Methods with Meta-Learning -- Labelling imaging datasets on the basis of neuroradiology reports: a validation study -- Semi-Supervised Learning for Instrument Detection with a Class Imbalanced Dataset -- Paying Per-label Attention for Multi-label Extraction from Radiology Reports.
520 $a 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.
650 0 $a Artificial intelligence. $3 559380
650 0 $a Optical data processing. $3 639187
650 0 $a Application software. $3 528147
650 0 $a Bioinformatics. $3 583857
650 0 $a Pattern recognition. $3 1253525
650 1 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Image Processing and Computer Vision. $3 670819
650 2 4 $a Computer Appl. in Social and Behavioral Sciences. $3 669920
650 2 4 $a Computational Biology/Bioinformatics. $3 677363
650 2 4 $a Pattern Recognition. $3 669796
700 1 $a Cardoso, Jaime. $e editor. $1 https://orcid.org/0000-0002-3760-2473 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1264774
700 1 $a Van Nguyen, Hien. $e editor. $1 https://orcid.org/0000-0001-7280-2182 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1327984
700 1 $a Heller, Nicholas. $e editor. $1 https://orcid.org/0000-0001-8516-8707 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1300285
700 1 $a Henriques Abreu, Pedro. $e editor. $1 https://orcid.org/0000-0002-9278-8194 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1327985
700 1 $a Isgum, Ivana. $e editor. $1 https://orcid.org/0000-0003-1869-5034 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1327986
700 1 $a Silva, Wilson. $e editor. $1 https://orcid.org/0000-0002-4080-9328 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1327987
700 1 $a Cruz, Ricardo. $e editor. $1 https://orcid.org/0000-0002-5189-6228 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1327988
700 1 $a Pereira Amorim, Jose. $e editor. $1 https://orcid.org/0000-0002-9477-0078 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1327989
700 1 $a Patel, Vishal. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1303098
700 1 $a Roysam, Badri. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1303099
700 1 $a Zhou, Kevin. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1303100
700 1 $a Jiang, Steve. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1297517
700 1 $a Le, Ngan. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1303102
700 1 $a Luu, Khoa. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1303101
700 1 $a Sznitman, Raphael. $e editor. $1 https://orcid.org/0000-0001-6791-4753 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1283759
700 1 $a Cheplygina, Veronika. $e editor. $1 https://orcid.org/0000-0003-0176-9324 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1283765
700 1 $a Mateus, Diana. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1273639
700 1 $a Trucco, Emanuele. $e editor. $1 https://orcid.org/0000-0002-5055-0794 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1282890
700 1 $a Abbasi, Samaneh. $e editor. $1 https://orcid.org/0000-0003-4150-6414 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1327990
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030611651
776 0 8 $i Printed edition: $z 9783030611675
830 0 $a Image Processing, Computer Vision, Pattern Recognition, and Graphics ; $v 9219 $3 1253644
856 4 0 $u https://doi.org/10.1007/978-3-030-61166-8
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
912 $a ZDB-2-LNC
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)