國立虎尾科技大學 |

Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support = Second International Workshop, iMIMIC 2019, and 9th International Workshop, ML-CDS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support/ edited by Kenji Suzuki, Mauricio Reyes, Tanveer Syeda-Mahmood, Ender Konukoglu, Ben Glocker, Roland Wiest, Yaniv Gur, Hayit Greenspan, Anant Madabhushi.
其他題名:	Second International Workshop, iMIMIC 2019, and 9th International Workshop, ML-CDS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings /
其他作者:	Suzuki, Kenji.
面頁冊數:	XVI, 93 p. 68 illus., 35 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Artificial intelligence. -
電子資源:	https://doi.org/10.1007/978-3-030-33850-3
ISBN:	9783030338503

Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support = Second International Workshop, iMIMIC 2019, and 9th International Workshop, ML-CDS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings /
Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision SupportSecond International Workshop, iMIMIC 2019, and 9th International Workshop, ML-CDS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings /[electronic resource] :edited by Kenji Suzuki, Mauricio Reyes, Tanveer Syeda-Mahmood, Ender Konukoglu, Ben Glocker, Roland Wiest, Yaniv Gur, Hayit Greenspan, Anant Madabhushi. - 1st ed. 2019. - XVI, 93 p. 68 illus., 35 illus. in color.online resource. - Image Processing, Computer Vision, Pattern Recognition, and Graphics ;11797. - Image Processing, Computer Vision, Pattern Recognition, and Graphics ;9219.

Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC 2019) -- Testing the robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer's disease classification -- UBS: A Dimension-Agnostic Metric for Concept Vector Interpretability Applied to Radiomics -- Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis -- Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection -- Guideline-based Additive Explanation for Computer-Aided Diagnosis of Lung Nodules -- Deep neural network or dermatologist? -- Towards Interpretability of Segmentation Networks by analyzing DeepDreams -- 9th International Workshop on Multimodal Learning for Clinical Decision Support (ML-CDS 2019) -- Towards Automatic Diagnosis from Multi-modal Medical Data -- Deep Learning based Multi-Modal Registration for Retinal Imaging -- Automated Enriched Medical Concept Generation for Chest X-ray Images.

This book constitutes the refereed joint proceedings of the Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2019, and the 9th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 7 full papers presented at iMIMIC 2019 and the 3 full papers presented at ML-CDS 2019 were carefully reviewed and selected from 10 submissions to iMIMIC and numerous submissions to ML-CDS. 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. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning. .

ISBN: 9783030338503

Standard No.: 10.1007/978-3-030-33850-3doiSubjects--Topical Terms:

559380
Artificial intelligence.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support = Second International Workshop, iMIMIC 2019, and 9th International Workshop, ML-CDS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings /
LDR:03811nam a22004095i 4500 001 1010171
003 DE-He213
005 20201007191529.0
007 cr nn 008mamaa
008 210106s2019 gw | s |||| 0|eng d
020 $a 9783030338503 $9 978-3-030-33850-3
024 7 $a 10.1007/978-3-030-33850-3 $2 doi
035 $a 978-3-030-33850-3
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 Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support $h [electronic resource] : $b Second International Workshop, iMIMIC 2019, and 9th International Workshop, ML-CDS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings / $c edited by Kenji Suzuki, Mauricio Reyes, Tanveer Syeda-Mahmood, Ender Konukoglu, Ben Glocker, Roland Wiest, Yaniv Gur, Hayit Greenspan, Anant Madabhushi.
250 $a 1st ed. 2019.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a XVI, 93 p. 68 illus., 35 illus. in color. $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 11797
505 0 $a Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC 2019) -- Testing the robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer's disease classification -- UBS: A Dimension-Agnostic Metric for Concept Vector Interpretability Applied to Radiomics -- Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis -- Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection -- Guideline-based Additive Explanation for Computer-Aided Diagnosis of Lung Nodules -- Deep neural network or dermatologist? -- Towards Interpretability of Segmentation Networks by analyzing DeepDreams -- 9th International Workshop on Multimodal Learning for Clinical Decision Support (ML-CDS 2019) -- Towards Automatic Diagnosis from Multi-modal Medical Data -- Deep Learning based Multi-Modal Registration for Retinal Imaging -- Automated Enriched Medical Concept Generation for Chest X-ray Images.
520 $a This book constitutes the refereed joint proceedings of the Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2019, and the 9th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 7 full papers presented at iMIMIC 2019 and the 3 full papers presented at ML-CDS 2019 were carefully reviewed and selected from 10 submissions to iMIMIC and numerous submissions to ML-CDS. 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. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning. .
650 0 $a Artificial intelligence. $3 559380
650 0 $a Mathematical logic. $2 bicssc $3 810627
650 0 $a Health informatics. $3 1064466
650 0 $a Optical data processing. $3 639187
650 1 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Mathematical Logic and Formal Languages. $3 670059
650 2 4 $a Health Informatics. $3 593963
650 2 4 $a Image Processing and Computer Vision. $3 670819
700 1 $a Suzuki, Kenji. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 680919
700 1 $a Reyes, Mauricio. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1267681
700 1 $a Syeda-Mahmood, Tanveer. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 890554
700 1 $a Konukoglu, Ender. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1304213
700 1 $a Glocker, Ben. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1254934
700 1 $a Wiest, Roland. $e editor. $1 https://orcid.org/0000-0001-7030-2045 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1304214
700 1 $a Gur, Yaniv. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1304215
700 1 $a Greenspan, Hayit. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 890553
700 1 $a Madabhushi, Anant. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 815885
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030338497
776 0 8 $i Printed edition: $z 9783030338510
830 0 $a Image Processing, Computer Vision, Pattern Recognition, and Graphics ; $v 9219 $3 1253644
856 4 0 $u https://doi.org/10.1007/978-3-030-33850-3
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)