MedGIFT
Topic: SCL TARGET
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Publications for topic "SCL TARGET"
2026
| , , , , , , , , , , , , and , A multi-modal deep learning network for the classification of paramagnetic rim and remyelinated lesions in multiple sclerosis (2026), in: Multiple Sclerosis Journal |
| , , , , , , , , , , , and , Impact of CT dose on AI performance: A comparison of radiomics, deep, and foundation models in a multi-centric anthropomorphic phantom study (2026), in: Medical Physics |
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| , , , , , , , and , Instance-level quantitative saliency in multiple sclerosis lesion segmentation (2026), in: Nature Scientific Reports |
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2025
| , , , , , , and , AI-based response assessment and prediction in longitudinal imaging for brain metastases treated with stereotactic radiosurgery, in: Learning with Longitudinal Medical Images and Data at MICCAI 2025, 2025 |
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| , , , , , , and , Exploiting XAI maps to improve MS lesion segmentation and detection in MRI, in: Workshop on Interpretability of Machine Intelligence in Medical Image Computing at MICCAI, 2025 |
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| , , , , , , and , The value of AI for assessing longitudinal brain metastases treatment response (2025), in: Neuro-Oncology Advances, 7:1 |
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2024
| , , , , and , A Bispectral 3D UNet for Rotation Robustness in Medical Segmentation, in: The First Workshop on Topology- and Graph-Informed Imaging Informatics at MICCAI, pages 43-54, Springer Nature Switzerland, 2024 |
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| , , , , , , , , and , Automatic Detection and Multi-Component Segmentation of Brain Metastases in Longitudinal MRI (2024), in: Nature Scientic Reports, 14:1(1-10) |
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| , , , , , , , , , and , Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [82Rb] PET for MACE prediction (2024), in: Nature Scientific Reports, 14:9644 |
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| , , , , and , Making sense of radiomics: Insights on human-AI collaboration in medical interaction from an observational user study (2024), in: Frontiers in Communication, 8 |
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2023
, , , , , , , , , , and , Automatic Head and Neck Tumor Segmentation and Outcome Prediction Relying on FDG-PET/CT Images: Findings from the Second Edition of the HECKTOR Challenge (2023), in: Medical Image Analysis, 90:1(102972)
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