Machine learning in medical imaging Part 2
Robust Box Prompt based SAM for Medical Image Segmentation.- Multi-task Learning Approach for Intracranial Hemorrhage Prognosis.- Mitigating False Predictions In Unreasonable Body Regions.- UniFed: A Universal Federation of a Mixture of Highly Heterogeneous Medical Image Classification Tasks.- Tackl...
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| Weitere Verfasser: | , , , , |
| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
Cham
Springer
2025
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| Schriftenreihe: | Lecture notes in computer science
15242 |
| Schlagworte: |
Biology, life sciences
> Biomedical engineering
> Biomedizinische Technik
> COM094000
> COMPUTERS / Computer Vision & Pattern Recognition
> COMPUTERS / Data Processing / Optical Data Processing
> Computer modelling & simulation
> Computer vision
> DV-gestützte Biologie/Bioinformatik
> Machine learning
> Maschinelles Lernen
> Maschinelles Sehen, Bildverstehen
> Mustererkennung
> Pattern recognition
> SCI102000
> TECHNOLOGY & ENGINEERING / Biomedical
> Konferenzschrift
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| Zusammenfassung: | Robust Box Prompt based SAM for Medical Image Segmentation.- Multi-task Learning Approach for Intracranial Hemorrhage Prognosis.- Mitigating False Predictions In Unreasonable Body Regions.- UniFed: A Universal Federation of a Mixture of Highly Heterogeneous Medical Image Classification Tasks.- Tackling domain generalization for out-of-distribution endoscopic imaging.- Benchmarking Dependence Measures to Prevent Shortcut Learning in Medical Imaging.- Selective Classifier Based Search Space Shrinking for Radiographs Retrieval.- Pseudo-Rendering for Resolution and Topology-Invariant Cortical Parcellation.- Partially Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation.- VIS-MAE: An Efficient Self-Supervised Learning Approach on Medical Image Segmentation and Classification.- Transformer-based Parameter Fitting of Models derived from Bloch-McConnell Equations for CEST MRI Analysis.- Probabilistic 3D Correspondence Prediction from Sparse Unsegmented Images.- StoDIP: Efficient 3D MRF image reconstruction with deep image priors and stochastic iterations.- Detection of Emerging Infectious Diseases in Lung CT based on Spatial Anomaly Patterns.- Data Alchemy: Mitigating Cross-Site Model Variability Through Test Time Data Calibration.- Noise-robust onformal prediction for medical image classification.- Identifying Critical Tokens for Accurate Predictions in Transformer-based Medical Imaging Models.-Resource-efficient Medical Image Analysis with Self-adapting Forward-Forward Networks.- SDF-Net: A Hybrid Detection Network for Mediastinal Lymph Node Detection on Contrast CT Images.- Arges: Spatio-Temporal Transformer for Ulcerative Colitis Severity Assessment in Endoscopy Videos.- Characterizing the Histology Spatial Intersections between Tumor-infiltrating Lymphocytes and Tumors for Survival Prediction of Cancers Via Graph Contrastive Learning.-Identifying Nonalcoholic Fatty Liver Disease and Adanced Liver Fibrosis from MRI in UK Biobank.- Explainable and Controllable Motion Curve Guided Cardiac Ultrasound Video Generation. This book constitutes the proceedings of the 15th International Workshop on Machine Learning in Medical Imaging, MLMI 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, on October 6, 2024. The 63 full papers presented in this volume were carefully reviewed and selected from 100 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging using artificial intelligence (AI) and machine learning (ML) |
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| Beschreibung: | Literaturangaben |
| Beschreibung: | xix, 247 Seiten Illustrationen, Diagramme |
| ISBN: | 9783031732928 978-3-031-73292-8 |