|
|
|
|
| LEADER |
00000cam a22000007i 4500 |
| 001 |
on1401632517 |
| 003 |
OCoLC |
| 005 |
20240909213021.0 |
| 006 |
m o d |
| 007 |
cr un|---aucuu |
| 008 |
231003s2023 sz a o 101 0 eng d |
| 040 |
|
|
|a GW5XE
|b eng
|e rda
|e pn
|c GW5XE
|d EBLCP
|d OCLCF
|d OCLCO
|d OCLCQ
|d WSU
|d OCLCO
|
| 020 |
|
|
|a 9783031439070
|q (electronic bk.)
|
| 020 |
|
|
|a 3031439074
|q (electronic bk.)
|
| 020 |
|
|
|z 9783031439063
|q (print)
|
| 024 |
7 |
|
|a 10.1007/978-3-031-43907-0
|2 doi
|
| 035 |
|
|
|a (OCoLC)1401632517
|
| 050 |
|
4 |
|a RC78.7.D53
|b I58 2023eb
|
| 072 |
|
7 |
|a UYT
|2 bicssc
|
| 072 |
|
7 |
|a COM016000
|2 bisacsh
|
| 072 |
|
7 |
|a UYT
|2 thema
|
| 049 |
|
|
|a HCDD
|
| 111 |
2 |
|
|a International Conference on Medical Image Computing and Computer-Assisted Intervention
|n (26th :
|d 2023 :
|c Vancouver, B.C. ; Online)
|
| 245 |
1 |
0 |
|a Medical image computing and computer assisted intervention - MICCAI 2023 :
|b 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings.
|n Part I /
|c Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor, editors.
|
| 246 |
3 |
|
|a MICCAI 2023
|
| 264 |
|
1 |
|a Cham :
|b Springer,
|c 2023.
|
| 300 |
|
|
|a 1 online resource (xxxviii, 785 pages) :
|b illustrations (some color).
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 490 |
1 |
|
|a Lecture Notes in Computer Science,
|x 1611-3349 ;
|v 14220
|
| 520 |
|
|
|a The ten-volume set LNCS 14220, 14221, 14222, 14223, 14224, 14225, 14226, 14227, 14228, and 14229 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023. The 730 revised full papers presented were carefully reviewed and selected from a total of 2250 submissions. The papers are organized in the following topical sections: Part I: Machine learning with limited supervision and machine learning - transfer learning; Part II: Machine learning -- learning strategies; machine learning -- explainability, bias, and uncertainty; Part III: Machine learning -- explainability, bias and uncertainty; image segmentation; Part IV: Image segmentation; Part V: Computer-aided diagnosis; Part VI: Computer-aided diagnosis; computational pathology; Part VII: Clinical applications -- abdomen; clinical applications -- breast; clinical applications -- cardiac; clinical applications -- dermatology; clinical applications -- fetal imaging; clinical applications -- lung; clinical applications -- musculoskeletal; clinical applications -- oncology; clinical applications -- ophthalmology; clinical applications -- vascular; Part VIII: Clinical applications -- neuroimaging; microscopy; Part IX: Image-guided intervention, surgical planning, and data science; Part X: Image reconstruction and image registration.
|
| 500 |
|
|
|a Includes author index.
|
| 588 |
0 |
|
|a Online resource; title from PDF title page (SpringerLink, viewed October 3, 2023).
|
| 505 |
0 |
|
|a Intro -- Preface -- Organization -- Contents - Part I -- Machine Learning with Limited Supervision -- PET-Diffusion: Unsupervised PET Enhancement Based on the Latent Diffusion Model -- 1 Introduction -- 2 Method -- 2.1 Image Compression -- 2.2 Latent Diffusion Model -- 2.3 Implementation Details -- 3 Experiments -- 3.1 Dataset -- 3.2 Ablation Analysis -- 3.3 Comparison with State-of-the-Art Methods -- 3.4 Generalization Evaluation -- 4 Conclusion and Limitations -- References -- MedIM: Boost Medical Image Representation via Radiology Report-Guided Masking -- 1 Introduction -- 2 Approach
|
| 505 |
8 |
|
|a 2.1 Image and Text Encoders -- 2.2 Report-Guided Mask Generation -- 2.3 Decoder for Reconstruction -- 2.4 Objective Function -- 2.5 Downstream Transfer Learning -- 3 Experiments and Results -- 3.1 Experimental Details -- 3.2 Comparisons with Different Pre-training Methods -- 3.3 Discussions -- 4 Conclusion -- References -- UOD: Universal One-Shot Detection of Anatomical Landmarks -- 1 Introduction -- 2 Method -- 2.1 Stage I: Contrastive Learning -- 2.2 Stage II: Supervised Learning -- 3 Experiment -- 3.1 Experimental Results -- 4 Conclusion -- References
|
| 505 |
8 |
|
|a S2ME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-Supervised Polyp Segmentation -- 1 Introduction -- 2 Methodology -- 2.1 Preliminaries -- 2.2 S2ME: Spatial-Spectral Mutual Teaching and Ensemble Learning -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Results and Analysis -- 3.3 Ablation Studies -- 4 Conclusion -- References -- Modularity-Constrained Dynamic Representation Learning for Interpretable Brain Disorder Analysis with Functional MRI -- 1 Introduction -- 2 Materials and Methodology -- 2.1 Subjects and Image Preprocessing -- 2.2 Proposed Method -- 3 Experiment
|
| 505 |
8 |
|
|a 4 Discussion -- 5 Conclusion and Future Work -- References -- Anatomy-Driven Pathology Detection on Chest X-rays -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Model -- 3.2 Inference -- 3.3 Training -- 3.4 Dataset -- 4 Experiments and Results -- 4.1 Experimental Setup and Baselines -- 4.2 Pathology Detection Results -- 5 Discussion and Conclusion -- References -- VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis -- 1 Introduction -- 2 Methods -- 3 Experimental Setup -- 4 Results -- 5 Conclusions -- References
|
| 505 |
8 |
|
|a Dense Transformer based Enhanced Coding Network for Unsupervised Metal Artifact Reduction -- 1 Introduction -- 2 Methodology -- 2.1 Hierarchical Disentangling Encoder (HDE) -- 2.2 Dense Transformer for Disentanglement (DTD) -- 2.3 Second-Order Disentanglement for MA Reduction (SOD-MAR) -- 2.4 Loss Function -- 3 Empirical Results -- 3.1 Ablation Study -- 3.2 Comparison to State-of-the-Art (SOTA) -- 4 Conclusion -- References -- Multi-scale Cross-restoration Framework for Electrocardiogram Anomaly Detection -- 1 Introduction -- 2 Method -- 2.1 Multi-scale Cross-restoration
|
| 650 |
|
0 |
|a Diagnostic imaging
|x Data processing
|v Congresses.
|
| 650 |
|
7 |
|a Diagnostic imaging
|x Data processing
|2 fast
|
| 655 |
|
7 |
|a proceedings (reports)
|2 aat
|
| 655 |
|
7 |
|a Conference papers and proceedings
|2 fast
|
| 655 |
|
7 |
|a Conference papers and proceedings.
|2 lcgft
|
| 655 |
|
7 |
|a Actes de congrès.
|2 rvmgf
|
| 700 |
1 |
|
|a Greenspan, Hayit,
|e editor.
|
| 700 |
1 |
|
|a Madabhushi, Anant,
|e editor.
|1 https://orcid.org/0000-0002-5741-0399
|
| 700 |
1 |
|
|a Mousavi, Parvin,
|e editor.
|
| 700 |
1 |
|
|a Salcudean, Septimiu Edmund,
|e editor.
|1 https://orcid.org/0000-0001-8826-8025
|
| 700 |
1 |
|
|a Duncan, James,
|d 1951-
|e editor.
|1 https://orcid.org/0000-0002-5167-9856
|
| 700 |
1 |
|
|a Syeda-Mahmood, Tanveer,
|e editor.
|1 https://orcid.org/0000-0003-0059-3208
|
| 700 |
1 |
|
|a Taylor, Russell,
|e editor.
|0 (orcid)0000-0001-6272-1100
|1 https://orcid.org/0000-0001-6272-1100
|
| 830 |
|
0 |
|a Lecture notes in computer science ;
|v 14220.
|x 1611-3349
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-031-43907-0
|y Click for online access
|
| 903 |
|
|
|a SPRING-ALL2023
|
| 994 |
|
|
|a 92
|b HCD
|