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Cancer Prevention, Detection, and Intervention: Third MICCAI Workshop, CaPTion 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings 2025 ed. [Minkštas viršelis]

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  • Formatas: Paperback / softback, 243 pages, aukštis x plotis: 235x155 mm, 61 Illustrations, color; 8 Illustrations, black and white; XII, 243 p. 69 illus., 61 illus. in color., 1 Paperback / softback
  • Serija: Lecture Notes in Computer Science 15199
  • Išleidimo metai: 09-Oct-2024
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3031733754
  • ISBN-13: 9783031733758
Kitos knygos pagal šią temą:
Cancer Prevention, Detection, and Intervention: Third MICCAI Workshop, CaPTion 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings 2025 ed.Didesnis paveikslėlis
  • Formatas: Paperback / softback, 243 pages, aukštis x plotis: 235x155 mm, 61 Illustrations, color; 8 Illustrations, black and white; XII, 243 p. 69 illus., 61 illus. in color., 1 Paperback / softback
  • Serija: Lecture Notes in Computer Science 15199
  • Išleidimo metai: 09-Oct-2024
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3031733754
  • ISBN-13: 9783031733758
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9783031733758.html
Raktažodžiai:
This book constitutes the refereed proceedings of the Third International Workshop on Cancer Prevention Through Early Detection, CaPTion, held in conjunction with the 27th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco, on October 6, 2024.





The 22 full papers presented in this book were carefully reviewed and selected from 25 submissions. They were organized in topical sections as follows: Classification and characterization; detection and segmentation; cancer/early cancer detection, treatment and survival prognosis.
Classification and characterization.- Multi-center ovarian tumor
classification using hierarchical transformer-based multiple-instance
learning.- FoTNet Enables Preoperative Differentiation of Malignant Brain
Tumors with Deep Learning.- Classification of Endoscopy and Video Capsule
Images using Hybrid Model.- Multimodal Deep Learning-based Prediction of
Immune Checkpoint Inhibitor Efficacy in Brain Metastases.- Seeing More with
Less: Meta-Learning and Diffusion Models for Tumor Characterization in
Low-data Settings.- Performance Evaluation of Deep Learning and Transformer
Models Using Multimodal Data for Breast Cancer Classification.- Detection and
Segmentation.- On undesired emergent behaviors in compound prostate cancer
detection systems.- Optimizing Multi-Expert Consensus for Classification and
Precise Localization of Barretts Neoplasia.- Automated Hepatocellular
Carcinoma Analysis in Multi-Phase CT with Deep Learning.- Refining deep
learning segmentation maps with a local thresholding approach: application to
liver surface nodularity quantification in CT.- Uncertainty-Aware Deep
Learning Classification for MRI-based Prostate Cancer Detection.- Generalized
Polyp Detection from Colonoscopy frames Using proposed EDF-YOLO8 Network.-
AI-Assisted Laryngeal Examination System.- UltraWeak: Enhancing Breast
Ultrasound Cancer Detection with Deformable DETR and Weak
Supervision.- SelectiveKD: A semi-supervised framework for cancer detection
in DBT through Knowledge Distillation and Pseudo-labeling.- Cancer/Early
cancer detection, treatment, and survival prognosis.-AI Age Discrepancy: A
Novel Parameter for Frailty Assessment in Kidney Tumor Patients.- Deep Neural
Networks for Predicting Recurrence and Survival in Patients with Esophageal
Cancer After Surgery.- Treatment efficacy prediction of focused ultrasound
therapies using multi-parametric magnetic resonance imaging.- SurRecNet: A
Multi-Task Model with Integrating MRI and Diagnostic Descriptions for Rectal
Cancer Survival Analysis.- Improved prediction of recurrence after prostate
cancer radiotherapy using multimodal data and in silico
simulations.- AutoDoseRank: Automated Dosimetry-informed Segmentation Ranking
for Radiotherapy.- SurvCORN: Survival Analysis with Conditional Ordinal
Ranking Neural Network.