Image-Based Prediction of Retinal Disease Progression
Produktnummer:
1892ab99a3e7284faa8212973608d9abc2
| Themengebiete: | Artificial Intelligence Machine Learning age-related macular degeneration diabetic retinopathy domain generalization life and medical sciences ophthalmology progression prediction retinal image analysis |
|---|---|
| Veröffentlichungsdatum: | 27.04.2025 |
| EAN: | 9783031866500 |
| Sprache: | Englisch |
| Seitenzahl: | 224 |
| Produktart: | Kartoniert / Broschiert |
| Herausgeber: | El Habib Daho, Mostafa Quellec, Gwenolé Zeghlache, Rachid |
| Verlag: | Springer International Publishing |
| Untertitel: | MICCAI Challenges, DIAMOND 2024 and MARIO 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings |
Produktinformationen "Image-Based Prediction of Retinal Disease Progression"
This book constitutes the proceedings from the MICCAI Challenges, Device-Independent Diabetic Macular Edema Onset Prediction, DIAMOND 2024, and Monitoring Age-Related macular degeneration progression in Optical coherence tomography, MARIO 2024, held in conjunction with the 27th International conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco in October 2024.The 15 papers included in this book from MARIO 2024 were carefully reviewed and selected from 17 submissions, whereas the 6 papers included here from DIAMOND 2024 were carefully reviewed and selected from 8 submissions. These papers focus on a wide range of state-of-the-art deep learning approaches to derive patient specific rules for Diabetic retinopathy (DR) and age-related macular degeneration (AMD) progression prediction from retinal images.
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