國立虎尾科技大學 |

Computational Mathematics Modeling in Cancer Analysis = First International Workshop, CMMCA 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Computational Mathematics Modeling in Cancer Analysis/ edited by Wenjian Qin, Nazar Zaki, Fa Zhang, Jia Wu, Fan Yang.
其他題名:	First International Workshop, CMMCA 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings /
其他作者:	Yang, Fan.
面頁冊數:	X, 160 p. 59 illus., 56 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Computing Milieux. -
電子資源:	https://doi.org/10.1007/978-3-031-17266-3
ISBN:	9783031172663

Computational Mathematics Modeling in Cancer Analysis = First International Workshop, CMMCA 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings /
Computational Mathematics Modeling in Cancer AnalysisFirst International Workshop, CMMCA 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings /[electronic resource] :edited by Wenjian Qin, Nazar Zaki, Fa Zhang, Jia Wu, Fan Yang. - 1st ed. 2022. - X, 160 p. 59 illus., 56 illus. in color.online resource. - Lecture Notes in Computer Science,135741611-3349 ;. - Lecture Notes in Computer Science,9324.

Cellular Architecture on Whole Slide Images Allows the Prediction of Survival in Lung Adenocarcinoma -- Is More Always Better? Effects of Patch Sampling in Distinguishing Chronic Lymphocytic Leukemia from Transformation to Diffuse Large B-cell Lymphoma -- Repeatability of Radiomic Features against Simulated Scanning Position Stochasticity across Imaging Modalities and Cancer Subtypes: A Retrospective Multi-Institutional Study on Head-and-Neck Cases -- MLCN: Metric Learning Constrained Network for Whole Slide Image Classification with Bilinear Gated Attention Mechanism -- NucDETR: End-to-End Transformer for Nucleus Detection in Histopathology Images -- Self-supervised learning based on a pre-trained method for the subtype classification of spinal tumors -- CanDLE: Illuminating Biases in Transcriptomic Pan-Cancer Diagnosis -- Cross-Stream Interactions: Segmentation of Lung Adenocarcinoma Growth Patterns -- Modality-collaborative AI model Ensemble for Lung Cancer Early Diagnosis -- Clustering-based Multi-instance Learning Network for Whole Slide Image Classification -- Multi-task Learning-driven Volume and Slice Level Contrastive Learning for 3D Medical Image Classification -- Light Annotation Fine Segmentation: Histology Image Segmentation based on VGG Fusion with Global Normalisation CAM -- Tubular Structure-Aware Convolutional Neural Networks for Organ at Risks Segmentation in Cervical Cancer Radiotherapy -- Automatic Computer-aided Histopathologic Segmentation for Nasopharyngeal Carcinoma using Transformer Framework -- Accurate Breast Tumor Identification UsingComputational Ultrasound Image Features.

This book constitutes the proceedings of the First Workshop on Computational Mathematics Modeling in Cancer Analysis (CMMCA2022), held in conjunction with MICCAI 2022, in Singapore in September 2022. Due to the COVID-19 pandemic restrictions, the CMMCA2022 was held virtually. DALI 2022 accepted 15 papers from the 16 submissions that were reviewed. A major focus of CMMCA2022 is to identify new cutting-edge techniques and their applications in cancer data analysis in response to trends and challenges in theoretical, computational and applied aspects of mathematics in cancer data analysis.

ISBN: 9783031172663

Standard No.: 10.1007/978-3-031-17266-3doiSubjects--Topical Terms:

669921
Computing Milieux.

LC Class. No.: TA1501-1820

Dewey Class. No.: 006

Computational Mathematics Modeling in Cancer Analysis = First International Workshop, CMMCA 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings /
LDR:03797nam a22004215i 4500 001 1083417
003 DE-He213
005 20220919204602.0
007 cr nn 008mamaa
008 221228s2022 sz | s |||| 0|eng d
020 $a 9783031172663 $9 978-3-031-17266-3
024 7 $a 10.1007/978-3-031-17266-3 $2 doi
035 $a 978-3-031-17266-3
050 4 $a TA1501-1820
050 4 $a TA1634
072 7 $a UYT $2 bicssc
072 7 $a COM016000 $2 bisacsh
072 7 $a UYT $2 thema
082 0 4 $a 006 $2 23
245 1 0 $a Computational Mathematics Modeling in Cancer Analysis $h [electronic resource] : $b First International Workshop, CMMCA 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings / $c edited by Wenjian Qin, Nazar Zaki, Fa Zhang, Jia Wu, Fan Yang.
250 $a 1st ed. 2022.
264 1 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2022.
300 $a X, 160 p. 59 illus., 56 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 Lecture Notes in Computer Science, $x 1611-3349 ; $v 13574
505 0 $a Cellular Architecture on Whole Slide Images Allows the Prediction of Survival in Lung Adenocarcinoma -- Is More Always Better? Effects of Patch Sampling in Distinguishing Chronic Lymphocytic Leukemia from Transformation to Diffuse Large B-cell Lymphoma -- Repeatability of Radiomic Features against Simulated Scanning Position Stochasticity across Imaging Modalities and Cancer Subtypes: A Retrospective Multi-Institutional Study on Head-and-Neck Cases -- MLCN: Metric Learning Constrained Network for Whole Slide Image Classification with Bilinear Gated Attention Mechanism -- NucDETR: End-to-End Transformer for Nucleus Detection in Histopathology Images -- Self-supervised learning based on a pre-trained method for the subtype classification of spinal tumors -- CanDLE: Illuminating Biases in Transcriptomic Pan-Cancer Diagnosis -- Cross-Stream Interactions: Segmentation of Lung Adenocarcinoma Growth Patterns -- Modality-collaborative AI model Ensemble for Lung Cancer Early Diagnosis -- Clustering-based Multi-instance Learning Network for Whole Slide Image Classification -- Multi-task Learning-driven Volume and Slice Level Contrastive Learning for 3D Medical Image Classification -- Light Annotation Fine Segmentation: Histology Image Segmentation based on VGG Fusion with Global Normalisation CAM -- Tubular Structure-Aware Convolutional Neural Networks for Organ at Risks Segmentation in Cervical Cancer Radiotherapy -- Automatic Computer-aided Histopathologic Segmentation for Nasopharyngeal Carcinoma using Transformer Framework -- Accurate Breast Tumor Identification UsingComputational Ultrasound Image Features.
520 $a This book constitutes the proceedings of the First Workshop on Computational Mathematics Modeling in Cancer Analysis (CMMCA2022), held in conjunction with MICCAI 2022, in Singapore in September 2022. Due to the COVID-19 pandemic restrictions, the CMMCA2022 was held virtually. DALI 2022 accepted 15 papers from the 16 submissions that were reviewed. A major focus of CMMCA2022 is to identify new cutting-edge techniques and their applications in cancer data analysis in response to trends and challenges in theoretical, computational and applied aspects of mathematics in cancer data analysis.
650 2 4 $a Computing Milieux. $3 669921
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Computer Engineering and Networks. $3 1365733
650 1 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 671334
650 0 $a Computers. $3 565115
650 0 $a Machine learning. $3 561253
650 0 $a Computer networks . $3 1365720
650 0 $a Computer engineering. $3 569006
650 0 $a Computer vision. $3 561800
650 0 $a Image processing—Digital techniques. $3 1365735
700 1 $a Yang, Fan. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 891977
700 1 $a Wu, Jia. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1281059
700 1 $a Zhang, Fa. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1136468
700 1 $a Zaki, Nazar. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1324851
700 1 $a Qin, Wenjian. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1389407
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783031172656
776 0 8 $i Printed edition: $z 9783031172670
830 0 $a Lecture Notes in Computer Science, $x 0302-9743 ; $v 9324 $3 1258960
856 4 0 $u https://doi.org/10.1007/978-3-031-17266-3
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)