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Produktbild: Efficient Medical Artificial Intelligence
Band 16318

Efficient Medical Artificial Intelligence First International Workshop, EMA4MICCAI 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings

Aus der Reihe Lecture Notes in Computer Science

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Beschreibung

This book constitutes the proceedings of the First International Workshop on Efficient Medical Artificial Intelligence, EMA4MICCAI 2025, held in conjunction with MICCAI 2025, in Daejeon, South Korea, on September 23, 2025.

The 36 full papers included in this book were carefully reviewed and selected...

Beschreibung

This book constitutes the proceedings of the First International Workshop on Efficient Medical Artificial Intelligence, EMA4MICCAI 2025, held in conjunction with MICCAI 2025, in Daejeon, South Korea, on September 23, 2025.


The 36 full papers included in this book were carefully reviewed and selected from 40 submissions. The EMA4MICCAI Workshop focuses on advancing computational effciency in medical AI by exploring innovative techniques such as lightweight model architectures, resource-conscious deployment strategies, and energy-effcient computing. 

Produktdetails

Einband

Taschenbuch
Erscheinungsdatum

03.01.2026
Abbildungen

XII, 373 p. 109 illus., 108 illus. in color.
Herausgeber Tong Chen + weitere

Produktdetails

Einband

Taschenbuch
Erscheinungsdatum

03.01.2026
Abbildungen

XII, 373 p. 109 illus., 108 illus. in color.
Herausgeber
Verlag Springer
Seitenzahl

373
Maße (L/B/H)

23,5/15,5/2,1 cm
Gewicht

587 g
Sprache

Englisch
ISBN

978-3-032-13960-3
Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: ProductSafety@springernature.com

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  • Produktbild: Efficient Medical Artificial Intelligence

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    .-  EL-UNet: An Efficient and Lightweight U-Net with Multi-Scale Attention for Medical Image Segmentation.



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    .- From O(n2) to O(n) Parameters: Quantum Self-Attention in Vision Transformers for Biomedical Image Classification.



    .- Human-in-the-Loop Active Learning for Real-Time Endoscopic Diagnostics on Edge Devices.



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    .-  Towards Efficient and Privacy-Preserving Medical Image Segmentation:A Point-Driven Source-Free Active Adaptation Framework.



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    .-  SAM 2 in Robotic Surgery: An Empirical Evaluation for Robustness and Generalization in Surgical Video Segmentation.



    .- EcoScale-Net: A Lightweight Multi-Kernel Network for Long-Sequence 12-lead ECG Classification.



    .-  Impact of Clinical Image Quality on Efficient Foundation Model Finetuning.



    .- Continual Multiple Instance Learning for Hematologic Disease Diagnosis.



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    .-  Interpretability-Aware Pruning for Efficient Medical Image Analysis.



    .-  PeekNet: A Power and Efficiency-Enhanced Knowledge-Aware Network for Real-Time Capsule Endoscopy Image Classification.



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    .-  niiv: Interactive Self-supervised Neural Implicit Isotropic Volume Reconstruction.



    .-  RARE-UNet: Resolution-Aligned Routing Entry for Adaptive Medical Image Segmentation.



    .-  Graph-based LLM over Semi-Structured Population Data for Dynamic Policy Response.



    .- Neural Cellular Automata for Weakly Supervised Segmentation of White Blood Cells.



    .- EndoSfM3D: Learning to 3D Reconstruct Any Endoscopic Surgery Scene using Self-supervised Foundation Model .



    .- WeakSupCon: Weakly Supervised Contrastive Learning for Encoder Pre-training.



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    .-  Debunking Optimization Myths in Federated Learning for Medical Image Classification.



    .- A Staining Variability-Aware Semi-Supervised Framework for H&E-to-IHC Virtual Staining.



    .- Counterfactual Augmentation for Long-Tailed Multi-Label Chest X-ray Classification.



    .- Domain-Incremental Continual Learning for Robust Surgical Tool Segmentation.