Institute of Informatics (II)
Topic: MedGIFT
Publications for topic "MedGIFT"
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2024
Automatic Detection and Multi-Component Segmentation of Brain Metastases in Longitudinal MRI (2024), in: Nature Scientic Reports | , , , , , , , , 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 | , , , , , , , , , and ,
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Instance-level explanations in multiple sclerosis lesion segmentation: a novel localized saliency map, in: ISMRM 2024, 2024 | , , , , , , , and ,
Instance-level quantitative saliency in multiple sclerosis lesion segmentation (2024), in: arxiv | , , , , , , , and ,
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Making sense of radiomics: Insights on human-AI collaboration in medical interaction from an observational user study (2024), in: Frontiers in Communication, 8 | , , , , and ,
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The image biomarker standardization initiative: Standardized convolutional filters for reproducible radiomics and enhanced clinical insights (2024), in: Radiology, 310:2
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2023
3D-Printed Iodine-Ink CT Phantom for Radiomics Feature Extraction - Advantages and Challenges (2023), in: Medical Physics, 50:9(5682-5697)
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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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FLAIR vs MPRAGE contribution to white matter lesion automatic segmentation in MS using localized saliency maps, in: Bern Interpretable AI Symposium (BIAS), 2023 | , , , , , , , and ,
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How far MS lesion detection and segmentation are integrated into the clinical workflow? A systematic review (2023), in: NeuroImage: Clinical, 39(103491)
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Identification of paramagnetic rim lesions using conventional MRI - a deep learning approach, in: 39th Congress Of The European Committee For Treatment And Research In Multiple Sclerosis (ECTRIMS), 2023 | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , and ,
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Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT, pages 1-30, Springer, Cham, 2023 | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , and ,
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QuantImage v2: A Comprehensive and Integrated Physician-Centered Cloud Platform for Radiomics and Machine Learning Research (2023), in: European Radiology Experimental, 7:16
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Rethinking the Role of AI with Physicians in Oncology: Revealing Perspectives from Clinical and Research Workflows, in: ACM CHI 2023, 2023 | , , , , , , and ,
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2022
A Global Taxonomy of Interpretable AI: Unifying the Terminology for the Technical and Social Sciences (2022), in: Artificial Intelligence Review, 56(3473–3504) | , , , , , , , , , , , , , , and ,
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Assessing radiomics feature stability with simulated CT acquisitions (2022), in: Scientific Reports, 12:1(4732)
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Cleaning Radiotherapy Contours for Radiomics Studies, is it Worth it? A Head and Neck Cancer Study (2022), in: Clinical and Translational Radiation Oncology, 33(153-158)
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Comparison of MR preprocessing strategies and sequences for radiomics-based MGMT prediction, in: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (MICCAI/BrainLes 2021), Cham, pages 367–380, Springer International Publishing, 2022 | , , , , , , , and ,
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Head and Neck Tumor Segmentation and Outcome Prediction, Springer International Publishing, 2022 |
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HEad and neCK TumOR segmentation and outcome prediction: The HECKTOR challenge, in: European Society of Radiology, 2022 | , , , , , , , and ,
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Head and Neck Tumor Segmentation in PET/CT: The HECKTOR Challenge (2022), in: Medical Image Analysis, 77(102336)
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Multi-Organ Nucleus Segmentation Using a Locally Rotation Invariant Bispectral U-Net, in: Medical Imaging with Deep Learning, 2022 | , , , and ,
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Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images, in: Head and Neck Tumor Segmentation and Outcome Prediction, pages 1-37, 2022 | , , , , , , , , , and ,
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QuantImage v2: A Clinician-in-the-loop Cloud Platform for Radiomics Research, in: European Society of Radiology, 2022 | , , , , , , , and ,
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Reproducibility of lung cancer radiomics features extracted from data-driven respiratory gating and free-breathing flow imaging in [18F]-FDG PET/CT (2022), in: European Journal of Hybrid Imaging, 6:1(33)
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Segmentation and Classification of Head and Neck Nodal Metastases and Primary Tumors in PET/CT, in: 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pages 4731-4735, 2022 | , , , , and ,
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Steer'n'Detect: Fast 2D Template Detection with Accurate Orientation Estimation (2022), in: Bioinformatics, 38:11(3146–3148)
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The Image Biomarker Standardisation Initiative (IBSI) On Reproducible Convolutional Radiomics, in: European Society of Radiology, 2022 | , , , , , and ,
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2021
Assessment of the stability and discriminative power of radiomics features in liver lesions using an anthropomorphic 3D-printed CT phantom, in: Scientific session SGR-SSR, 2021 | , , , , , , , , and ,
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Fully Automatic Head and Neck Cancer Prognosis Prediction in PET/CT, in: Multimodal Learning for Clinical Decision Support, pages 59-68, Springer LNCS, 2021 | , , , , , and ,
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Multi-Task Deep Segmentation and Radiomics for Automatic Prognosis in Head and Neck Cancer, in: 4th Workshop on PRedictive Intelligence in MEdicine, pages 147-156, Springer LNCS, 2021 | , , , , , and ,
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On the Scale Invariance in State of the Art CNNs Trained on ImageNet (2021), in: Special Issue "Interpretable and Annotation-Efficient Learning for Medical Image Computing" in Machine Learning and Knowledge Extraction:3(374–391) | , , , and ,
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Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT, pages 1-21, 2021 | , , , , , , and ,
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Principled Design and Implementation of Steerable Detectors (2021), in: IEEE Transactions on Image Processing, 30(4465-4478)
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QuantImage v2: an Open-Source and Web-Based Integrated Platform for Clinical Radiomics Research, in: Joint scientific session SSRMP/SGR-SSR, 2021 | , , , and ,
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Revealing most suitable CT radiomics features for retrospective studies with heterogeneous datasets, in: European Congress of Radiology (ECR) 2021, ONLINE edition, 2021 | , , , , , , , , and ,
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The discriminative power and stability of radiomics features with CT variations: Task-based analysis in an anthropomorphic 3D-printed CT phantom (2021), in: Investigative Radiology, 56:12(820-825)
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2020
3D Solid Spherical Bispectrum CNNs for Biomedical Texture Analysis, 2020
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A lung graph model for the radiological assessment of chronic thromboembolic pulmonary hypertension in CT (2020), in: Computers in Biology and Medicine(103962)
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Automatic Segmentation of Head and Neck Tumors and Nodal Metastases in PET-CT scans, in: Medical Imaging with Deep Learning, Montréal, Canada, 2020 | , , , , , , , and ,
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Consistency of Scale Covariance in Internal Representations of CNNs, in: Irish Machine Vision and Image Processing Conference, 2020 | , , and ,
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Evaluation of the Prognostic Value of FDG PET/CT Parameters for Patients with Surgically Treated Head and Neck Cancer: A Systematic Review (2020), in: JAMA Otolaryngology - Head and Neck Surgery, 146:5(471-479)
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Exploiting the PubMed Central repository to mine out a large multimodal dataset of rare cancer studies, in: SPIE Medical Imaging, Houston, TX, USA, 2020 | , , , and ,
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Integrating radiomics into holomics for personalised oncology: from algorithms to bedside (2020), in: European Radiology Experimental, 4(11) | , , , and ,
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Interpretable CNN Pruning for Preserving Scale-Covariant Features in Medical Imaging, in: Workshop on Interpretability of Machine Intelligence in Medical Image Computing at MICCAI 2020, 2020 | , , , and ,
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Local Rotation Invariance in 3D CNNs (2020), in: Medical Image Analysis, 65(101756)
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Oropharynx Detection in PET-CT for Tumor Segmentation, in: Irish Machine Vision and Image Processing Conference, 2020, 2020 | , and ,
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Rationale and protocol of the StayFitLonger study: a multicentre trial to measure efficacy and adherence of a home-based computerised multidomain intervention in healthy older adults. (2020), in: BMC Geriatrics | , , , , , , , , , , and ,
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Standardised convolutional filtering for radiomics, 2020 | , , , , , , and ,
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Standardized quantitative radiomics for high-throughput image-based phenotyping (2020), in: Radiology, 295:2(328-338)
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| 1-50 | 51-100 | 101-150 | 151-200 | 201-250 | 251-300 | 301-350 | 351-400 | 401-450 | 451-500 | 501-550 | 551-590 |