Empowering Medical Image Computing and Research Through Early-Career Expertise

Second MICCAI Student Board Workshop, EMERGE 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings
Erscheinungsjahr 2026
ISBN: 978-3-032-24181-8
Verlag: Springer

Buch, Englisch, Band 16534, 146 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 248 g

Reihe: Lecture Notes in Computer Science

This book presents a series of revised papers selected from the Second MICCAI Student Board Workshop, EMERGE 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings.

The 13 full papers presented in this book were carefully reviewed and selected from 19 submissions. These papers were organized in the following topical sections: Foundation Models and Generalization in Medical AI; Representation Learning for Detection and Diagnosis; Signals, Bias, and Structure in Medical Data; Poster Presentations.

Zielgruppe

Research

Autoren/Hrsg.

Akash, Naren

Bercea, Cosmin

Kumar, Amar

Fachgebiete

• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe

Inhaltsverzeichnis

.- Foundation Models and Generalization in Medical AI.
.- NFCMTL: Auto NailFold Capillaroscopy through a Multi-Task Learning Model.
.- Foundation Models as Class-Incremental Learners for Dermatological Image Classification.
.- ZeroSlide: Is Zero-Shot Classification Adequate for Lifelong Learning in Whole-Slide Image Analysis in the Era of Pathology Vision-Language Foundation Models?.
.- Representation Learning for Detection and Diagnosis.
.- GroundingDINO for Open-Set Lesion Detection in Medical Imaging.
.- Structured Spectral Graph Learning for Anomaly Classification in 3D Chest CT Scans.
.- Automated Method Design for Cancer Image Classification by Differential Evolution and Ensembling.
.- Signals, Bias, and Structure in Medical Data.
.- A Study in Scatter: Investigating Low-Contrast Image Contents Outside the X-Ray Collimation.
.- ESCAViT: Symmetry-Aware EEG Classification.
.- Priority-Aware Clinical Pathology Hierarchy Training for Multiple Instance Learning.
.- Poster Presentations.
.- Invisible Yet Detected: PelFANet with Attention-Guided Anatomical Fusion for Pelvic Fracture Diagnosis.
.- XBoundNet++: Uncertainty-Aware Segmentation of Kidney Ablation Zones.
.- Self-supervised Vision Transformers for Prostate Cancer Classification in Biparametric MRI.
.- Memory-Enhanced Temporal Learning: Leveraging SAM2’s Memory Modules for Consistent Video Segmentation on Surgical Video.