Buch, Englisch, Band 15197, 104 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 189 g
Artificial Intelligence in Pancreatic Disease Detection and Diagnosis, and Personalized Incremental Learning in Medicine
2025
ISBN: 978-3-031-73482-3
Verlag: Springer Nature Switzerland
First International Workshop, AIPAD 2024 and First International Workshop, PILM 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings
Buch, Englisch, Band 15197, 104 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 189 g
Reihe: Lecture Notes in Computer Science
ISBN: 978-3-031-73482-3
Verlag: Springer Nature Switzerland
This volume constitutes the refereed proceedings of the First International Workshop on Artificial Intelligence in Pancreatic Disease Detection and Diagnosis, AIPAD 2024 and the First International Workshop on Personalized Incremental Learning in Medicine, PILM 2024, held in conjunction with MICCAI 2024, in Marrakesh, Morocco, in October 2024.
The 8 full papers included in these proceedings were carefully reviewed and selected from 9 submissions. They were organized in topical sections as follows: artificial intelligence in pancreatic disease detection and diagnosis; and personalized incremental learning in medicine.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Artificial Intelligence in Pancreatic Disease Detection and Diagnosis.- Assessing the Efficacy of Foundation Models in Pancreas Segmentation.- Hybrid Deep Learning Model for Pancreatic Cancer Image Segmentation.- Leveraging SAM and Learnable Prompts for Pancreatic MRI Segmentation.- Optimizing Synthetic Data for Enhanced Pancreatic Tumor Segmentation.- Pancreatic Vessel Landmark Detection in CT Angiography using Prior Anatomical Knowledge.- Personalized Incremental Learning in Medicine.-Addressing Catastrophic Forgetting by Modulating Global Batch Normalization Statistics for Medical Domain Expansion.- Distribution-Aware Replay for Continual MRI Segmentation.- Exploring Wearable Emotion Recognition with Transformer-Based Continual Learning.
