國立高雄大學圖資館 |

登入

回首頁

Foundation models for general medical AIsecond International Workshop, MedAGI 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024 : proceedings /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Foundation models for general medical AIedited by Zhongying Deng ... [et al.].
其他題名:	second International Workshop, MedAGI 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024 : proceedings /
其他題名:	MedAGI 2024
其他作者:	Deng, Zhongying.
團體作者:
出版者:	Cham :Springer Nature Switzerland :2025.
面頁冊數:	x, 174 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Diagnostic imagingCongresses.
電子資源:	https://doi.org/10.1007/978-3-031-73471-7
ISBN:	9783031734717$q(electronic bk.)

Foundation models for general medical AIsecond International Workshop, MedAGI 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024 : proceedings /
Foundation models for general medical AIsecond International Workshop, MedAGI 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024 : proceedings /[electronic resource] :MedAGI 2024edited by Zhongying Deng ... [et al.]. - Cham :Springer Nature Switzerland :2025. - x, 174 p. :ill. (chiefly color), digital ;24 cm. - Lecture notes in computer science,151841611-3349 ;. - Lecture notes in computer science ;4891..

FastSAM-3DSlicer: A 3D-Slicer Extension for 3D Volumetric Segment Anything Model with Uncertainty Quantification. -- The Importance of Downstream Networks in Digital Pathology Foundation Models. -- Temporal-spatial Adaptation of Promptable SAM Enhance Accuracy and Generalizability of cine CMR Segmentation. -- Navigating Data Scarcity using Foundation Models: A Benchmark of Few-Shot and Zero-Shot Learning Approaches in Medical Imaging. -- AutoEncoder-Based Feature Transformation with Multiple Foundation Models in Computational Pathology. -- OSATTA: One-Shot Automatic Test Time Augmentation for Domain Adaptation. -- Automating MedSAM by Learning Prompts with Weak Few-Shot Supervision. -- SAT-Morph: Unsupervised Deformable Medical Image Registration using Vision Foundation Models with Anatomically Aware Text Prompt. -- Promptable Counterfactual Diffusion Model for Unified Brain Tumor Segmentation and Generation with MRIs. -- D- Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions. -- Optimal Prompting in SAM for Few-Shot and Weakly Supervised Medical Image Segmentation. -- UniCrossAdapter: Multimodal Adaptation of CLIP for Radiology Report Generation. -- TUMSyn: A Text-Guided Generalist model for Customized Multimodal MR Image Synthesis. -- SAMU: An Efficient and Promptable Foundation Model for Medical Image Segmentation. -- Anatomical Embedding-Based Training Method for Medical Image Segmentation Foundation Models. -- Boosting Vision-Language Models for Histopathology Classification: Predict all at once. -- MAGDA: Multi-agent guideline-driven diagnostic assistance.

This book constitutes the refereed proceedings from the Second International Workshop on Foundation Models for General Medical AI, MedAGI 2024, held in conjunction with the 27th International conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco in October 2024. The 17 papers included in this book were carefully reviewed and selected from 26 submissions. These papers provide insights into the current landscape of medical AI and foundation models, that can pave the way for the evolution of task-specific medical AI systems into more generalized frameworks capable of tackling a diverse range of tasks, datasets, and domains.

ISBN: 9783031734717$q(electronic bk.)

Standard No.: 10.1007/978-3-031-73471-7doiSubjects--Topical Terms:

387225
Diagnostic imaging
--Congresses.

LC Class. No.: RC78.7.D53

Dewey Class. No.: 616.0754

Foundation models for general medical AIsecond International Workshop, MedAGI 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024 : proceedings /
LDR:03570nmm a2200361 a 4500 001 678559
003 DE-He213
005 20241002130245.0
006 m d
007 cr nn 008maaau
008 251031s2025 sz s 0 eng d
020 $a 9783031734717$q(electronic bk.)
020 $a 9783031734700$q(paper)
024 7 $a 10.1007/978-3-031-73471-7 $2 doi
035 $a 978-3-031-73471-7
040 $a GP $c GP
041 0 $a eng
050 4 $a RC78.7.D53
072 7 $a UYT $2 bicssc
072 7 $a COM016000 $2 bisacsh
072 7 $a UYT $2 thema
082 0 4 $a 616.0754 $2 23
090 $a RC78.7.D53 $b M488 2024
111 2 $n (3rd : $d 1998 : $c Amsterdam, Netherlands) $3 194767
245 1 0 $a Foundation models for general medical AI $h [electronic resource] : $b second International Workshop, MedAGI 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024 : proceedings / $c edited by Zhongying Deng ... [et al.].
246 3 $a MedAGI 2024
246 3 $a MICCAI 2024
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a x, 174 p. : $b ill. (chiefly color), digital ; $c 24 cm.
490 1 $a Lecture notes in computer science, $x 1611-3349 ; $v 15184
505 0 $a FastSAM-3DSlicer: A 3D-Slicer Extension for 3D Volumetric Segment Anything Model with Uncertainty Quantification. -- The Importance of Downstream Networks in Digital Pathology Foundation Models. -- Temporal-spatial Adaptation of Promptable SAM Enhance Accuracy and Generalizability of cine CMR Segmentation. -- Navigating Data Scarcity using Foundation Models: A Benchmark of Few-Shot and Zero-Shot Learning Approaches in Medical Imaging. -- AutoEncoder-Based Feature Transformation with Multiple Foundation Models in Computational Pathology. -- OSATTA: One-Shot Automatic Test Time Augmentation for Domain Adaptation. -- Automating MedSAM by Learning Prompts with Weak Few-Shot Supervision. -- SAT-Morph: Unsupervised Deformable Medical Image Registration using Vision Foundation Models with Anatomically Aware Text Prompt. -- Promptable Counterfactual Diffusion Model for Unified Brain Tumor Segmentation and Generation with MRIs. -- D- Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions. -- Optimal Prompting in SAM for Few-Shot and Weakly Supervised Medical Image Segmentation. -- UniCrossAdapter: Multimodal Adaptation of CLIP for Radiology Report Generation. -- TUMSyn: A Text-Guided Generalist model for Customized Multimodal MR Image Synthesis. -- SAMU: An Efficient and Promptable Foundation Model for Medical Image Segmentation. -- Anatomical Embedding-Based Training Method for Medical Image Segmentation Foundation Models. -- Boosting Vision-Language Models for Histopathology Classification: Predict all at once. -- MAGDA: Multi-agent guideline-driven diagnostic assistance.
520 $a This book constitutes the refereed proceedings from the Second International Workshop on Foundation Models for General Medical AI, MedAGI 2024, held in conjunction with the 27th International conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco in October 2024. The 17 papers included in this book were carefully reviewed and selected from 26 submissions. These papers provide insights into the current landscape of medical AI and foundation models, that can pave the way for the evolution of task-specific medical AI systems into more generalized frameworks capable of tackling a diverse range of tasks, datasets, and domains.
650 0 $a Diagnostic imaging $v Congresses. $3 387225
650 0 $a Artificial intelligence $x Medical applications $v Congresses. $3 442992
650 0 $a Image processing $x Digital techniques. $3 182119
650 1 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 274492
650 2 4 $a Biological Imaging. $3 933220
700 1 $a Deng, Zhongying. $3 991967
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
856 4 0 $u https://doi.org/10.1007/978-3-031-73471-7
950 $a Computer Science (SpringerNature-11645)