Foundation Models for General Medical AI

Nyhet

Third International Workshop, MedAGI 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 27, 2025, Proceedings

Häftad, Engelska, 2025

Av Won-Ki Jeong, Hyunwoo J. Kim, Zhongying Deng, Yiqing Shen, Angelica I Aviles-Rivero, Shaoting Zhang

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This book constitutes the refereed proceedings from the Third International Workshop on Foundation Models for General Medical AI, MedAGI 2025, held in conjunction with the 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, in Daejeon, South Korea, during September 27, 2025.The 19 full papers included in this book were carefully reviewed and selected from 31 submissions. The workshop aims to foster discussions that accelerate the transition from task-specific medical AI systems toward more generalized frameworks capable of addressing diverse tasks, datasets, and domains.

Produktinformation

Utgivningsdatum2025-10-12
Mått155 x 235 x 12 mm
Vikt335 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor204
FörlagSpringer Nature Switzerland AG
ISBN9783032078445

Tillhör följande kategorier

Analytisk kemi inom Naturvetenskap och teknik
Övrig teknik och tillämpad vetenskap inom Naturvetenskap och teknik
Systemvetenskap och AI inom Data och it
Hälsoinformatik inom Medicin
Biomedicinsk teknik inom Medicin
Artificiell intelligens inom Data och it

Lamps: Learning Anatomy from Multiple Perspectives via Self-supervision.- Segment Anything for Cell Tracking.- BioVFM-21M: Benchmarking and Scaling Self-Supervised Vision Foundation Models for Biomedical Image Analysis.- From Pathology to Radiology: Evaluating the Applicability of Pathology Foundation Models.- Pathology Foundation Models are Scanner Sensitive: Benchmark and Mitigation with Contrastive ScanGen Loss.- Improved Training Sample Efficiency and Inter-Device Generalizability in Optical Coherence Tomography Fluid Segmentation via Foundation Models.- Taming Stable Diffusion for Computed Tomography Blind Super-Resolution.- RadiSimCLIP: A Radiology Vision-Language Model Pretrained on Simulated Radiologist Learning Dataset for Zero-Shot Medical Image Understanding.- Improving Medical Visual Instruction Tuning with Labeled Datasets.- DR.SIMON: Domain-wise Rewrite for Segment-Informed MedicalOversight Network.- The Data Behind the Model: Gaps and Opportunities for Foundation Models in Brain Imaging.- LGE Scar Quantification Using Foundation Models for Cardiac Disease Classification.- Beyond Broad Applications: Can Pathology Foundation Models Adapt to Hematopathology.- EndoTracker: Robustly Tracking Any Point in Endoscopic Surgical Scene.- Temporally-Constrained Video Reasoning Segmentation and Automated Benchmark Construction.- Cross-Modal Knowledge Distillation for Chest Radiographic Diagnosis via Embedding Expansion, Reconstruction, and Classification.- Random Direct Preference Optimization for Radiography Report Generation.- Test Time Adaptation of Medical Vision-Language Models.- MaskedCLIP: Bridging the Masked and CLIP Space for Semi-Supervised Medical Vision-Language Pre-training.