國立高雄大學圖資館 |

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 /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Interpretable and annotation-efficient learning for medical image computingedited by Jaime Cardoso ... [et al.].
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 /
其他題名:	iMIMIC 2020
其他作者:	Cardoso, Jaime.
團體作者:
出版者:	Cham :Springer International Publishing :2020.
面頁冊數:	xxii, 292 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Diagnostic imagingCongresses.Data processing
電子資源:	https://doi.org/10.1007/978-3-030-61166-8
ISBN:	9783030611668$q(electronic bk.)

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 /
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] :iMIMIC 2020edited by Jaime Cardoso ... [et al.]. - Cham :Springer International Publishing :2020. - xxii, 292 p. :ill., digital ;24 cm. - Lecture notes in computer science,124460302-9743 ;. - Lecture notes in computer science ;4891..

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$q(electronic bk.)

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

445765
Diagnostic imaging
--Data processing--Congresses.

LC Class. No.: RC78.7.D53 / I55 2020

Dewey Class. No.: 616.0757

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 /
LDR:05427nmm a2200397 a 4500 001 586009
003 DE-He213
005 20201003203028.0
006 m d
007 cr nn 008maaau
008 210323s2020 sz s 0 eng d
020 $a 9783030611668$q(electronic bk.)
020 $a 9783030611651$q(paper)
024 7 $a 10.1007/978-3-030-61166-8 $2 doi
035 $a 978-3-030-61166-8
040 $a GP $c GP
041 0 $a eng
050 4 $a RC78.7.D53 $b I55 2020
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 616.0757 $2 23
090 $a RC78.7.D53 $b I32 2020
111 2 $n (3rd : $d 1998 : $c Amsterdam, Netherlands) $3 194767
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 ... [et al.].
246 3 $a iMIMIC 2020
246 3 $a MIL3iD 2020
246 3 $a LABELS 2020
246 3 $a MICCAI 2020
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xxii, 292 p. : $b ill., digital ; $c 24 cm.
490 1 $a Lecture notes in computer science, $x 0302-9743 ; $v 12446
490 1 $a Image processing, computer vision, pattern recognition, and graphics
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 Diagnostic imaging $x Data processing $v Congresses. $3 445765
650 0 $a Computer-assisted surgery $v Congresses. $3 470331
650 1 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Image Processing and Computer Vision. $3 274051
650 2 4 $a Computer Appl. in Social and Behavioral Sciences. $3 274376
650 2 4 $a Computational Biology/Bioinformatics. $3 274833
650 2 4 $a Pattern Recognition. $3 273706
700 1 $a Cardoso, Jaime. $3 877241
710 2 $a SpringerLink (Online service) $3 273601
711 2 $n (3rd : $d 1998 : $c Amsterdam, Netherlands) $3 194767
773 0 $t Springer Nature eBook
830 0 $a Lecture notes in computer science ; $v 4891. $3 383229
830 0 $a Image processing, computer vision, pattern recognition, and graphics. $3 823073
856 4 0 $u https://doi.org/10.1007/978-3-030-61166-8
950 $a Computer Science (SpringerNature-11645)