Trefwoorden:
- adversarial learning
- Automatic segmentation
- Challenge
- Classification
- Computer Vision and Pattern Recognition (cs.CV)
- concept vectors
- cs.CV
- curriculum learning
- Deep convolutional neural network
- Deep Learning
- explainable AI (XAI)
- eye fundus images
- Feature ranking
- feature reuse
- finetuning
- FOS: Computer and information sciences
- glaucoma diagnosis
- Global explainability
- head and neck cancer
- Histopathology
- HPV status explanation. TNM explanation
- human-machine interaction
- interpretability
- Local explainability
- machine learning
- Machine Learning (cs.LG)
- medical imaging
- morphometric features
- multi-task learning
- Natural Language Processing
- Open access
- Oropharynx
- rule extraction
- wrong labels
Publicaties van Vincent Andrearczyk gesorteerd op titel
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, , , en , 3D Solid Spherical Bispectrum CNNs for Biomedical Texture Analysis, 2020
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| , , , , en , A Bispectral 3D UNet for Rotation Robustness in Medical Segmentation, in: The First Workshop on Topology- and Graph-Informed Imaging Informatics at MICCAI, pagina's 43-54, Springer Nature Switzerland, 2024 |
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| , , , , , , , , , , , , , , en , A Global Taxonomy of Interpretable AI: Unifying the Terminology for the Technical and Social Sciences (2022), in: Artificial Intelligence Review, 56(3473–3504) |
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| , , , , , , , , , , , , en , A multi-modal deep learning network for the classification of paramagnetic rim and remyelinated lesions in multiple sclerosis (2026), in: Multiple Sclerosis Journal |
| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , en , A Multimodal and Multi-centric Head and Neck Cancer Dataset for Tumor Segmentation and Outcome Prediction (2025) |
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| , , , , en , A systematic comparison of deep learning strategies for weakly supervised Gleason grading,, in: SPIE Medical Imaging, Houstonm, TX, USA, 2020 |
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| , , , , , , , , , , en , AI-based Prediction of Myocardium Viability Using [82Rb] PET/CT, 2025 |
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| , , , , , , en , 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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| , , , , , , en , An Exploration of Uncertainty Information for Segmentation Quality Assessment, in: SPIE Medical Imaging 2020: Image Processing, Houston, TX, USA, pagina's 381-390, SPIE, 2020 |
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| , , , , en , Automated Tumor Segmentation in Radiotherapy (2022), in: Seminars in Radiation Oncology, 32:4(319-329) |
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| , , , , , , , , en , 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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, , , , , , , , , , en , 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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| , , , , , en , Automatic rib fracture detection on postmortem CT data using deep learning (2025), in: International Journal of Legal Medicine |
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| , , , , , , , en , Automatic Segmentation of Head and Neck Tumors and Nodal Metastases in PET-CT scans, in: Medical Imaging with Deep Learning, Montréal, Canada, 2020 |
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| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , en , Biomedical image analysis competitions: The state of current participation practice (2022), in: arXiv |
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| , , , , en , Breast Histopathology with High-Performance Computing and Deep Learning (2020), in: Computer and Informatics |
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, , , , , , , , , en , 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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| , , , , , , , , , en , 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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| , , , , , , , en , 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, pagina's 367–380, Springer International Publishing, 2022 |
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| , en , Concept attribution: Explaining CNN decisions to physicians (2020), in: Computers in Biology and Medicine |
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| , , , en , Concept discovery and Dataset exploration with Singular Value Decomposition, ICLR Workshop on Trustworthy ML, 2023 |
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| , , en , Consistency of Scale Covariance in Internal Representations of CNNs, in: Irish Machine Vision and Image Processing Conference, 2020 |
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| , en , Deep Learning Interpretability: Measureing the relevance of clinical concepts in CNN features, in: State of the art in neural networks, Elsevier, 2022 |
| , en , Deep learning interpretability: measuring the relevance of clinical concepts in convolutional neural networks features, Elsevier, deel 157-192, 2023 |
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| en , Deep Multimodal Classification of Image Types in Biomedical Journal Figures, in: CLEF 2018, Avignon, France, Springer, 2018 |
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| , , , , , , , , , , en , Deep-PRL: a deep learning network for the identification of paramagnetic rim lesions in multiple sclerosis, in: ISMRM 2025, 2025 |
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| , , en , Disentangling Neuron Representations with Concept Vectors, in: Proceedings of the 2nd Explainable AI for Computer Vision (XAI4CV) Workshop at CVPR 2023, 2023 |
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| , , en , Evaluation and Comparison of CNN Visual Explanations for Histopathology, in: XAI-AAAI-21, 2021 |
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| , , , , , , , , , , , en , Explainability in automatic Paramagnetic Rim Lesion classification, in: 40th Congress Of The European Committee For Treatment And Research In Multiple Sclerosis (ECTRIMS), 2024 |
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| , , , , en , Explanation Generation via Decompositional Rules Extraction for Head and Neck Cancer Classification, in: Explainable and Transparent AI and Multi-Agent Systems, 2023 |
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| , , , en , Exploiting the PubMed Central repository to mine out a large multimodal dataset of rare cancer studies, in: SPIE Medical Imaging, Houston, TX, USA, 2020 |
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| , , , , , , en , 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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, , , en , Exploring local rotation invariance in 3D CNNs with steerable filters, in: Medical Imaging with Deep Learning, pagina's 15-26, Proceedings of Machine Learning Research, 2019
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| , , en , Fast Rotational Sparse Coding (2018)(arXiv:1806.04374) |
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| , , , , , , , en , FLAIR vs MPRAGE contribution to white matter lesion automatic segmentation in MS using localized saliency maps, in: Bern Interpretable AI Symposium (BIAS), 2023 |
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| , , , , , en , Fully Automatic Head and Neck Cancer Prognosis Prediction in PET/CT, in: Multimodal Learning for Clinical Decision Support, pagina's 59-68, Springer LNCS, 2021 |
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| , , en , Generalizing Convolution Neural Networks on Stain Color Heterogeneous Data for Computational Pathology, in: SPIE Medical Imaging, Houston, TX, USA,, 2020 |
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| , , , en , Glaucoma Diagnosis from Eye Fundus Images Based on Deep Morphometric Feature Estimation, in: OMIA at MICCAI, Granada, Spain, 2018 |
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| , , en , Guiding CNNs towards Relevant Concepts by Multi-task and Adversarial Learning, arxiv, 2020 |
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| Head and Neck Tumor Segmentation, Springer International Publishing, 2021 |
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| Head and Neck Tumor Segmentation and Outcome Prediction, Springer International Publishing, 2022 |
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| , , en , Head and Neck Tumor Segmentation and Outcome Prediction, Springer International Publishing, 2023 |
[DOI] [URL] |
| , , , , , , en , HEad and neCK TumOR segmentation and outcome prediction using AI: lessons from three consecutive years of the HECKTOR challenge, in: European Head and Neck Society (EHNS) on Artificial Intelligence (AI) in Head & Neck Oncology, Lausanne and virtual, 2023 |
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| , , , , , , , en , HEad and neCK TumOR segmentation and outcome prediction: The HECKTOR challenge, in: European Society of Radiology, 2022 |
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, , , , , , , , , , , , , , , , , , , , , , , , , en , Head and Neck Tumor Segmentation in PET/CT: The HECKTOR Challenge (2022), in: Medical Image Analysis, 77(102336)
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| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , en , 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 |
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| , , en , Image Magnification Regression Using DenseNet for Exploiting Histopathology Open Access Content, in: MICCAI 2018 - Computational Pathology Workshop (COMPAY), Granada, Spain, 2018 |
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| , , , , , , , , , , , , , , , , , , , , , , , , en , ImageCLEF 2019: Multimedia Retrieval in Medical Nature, Security and Lifelogging Applications, in: ECIR 2019, Cologne, Germany, 2019 |
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| 1-50 | 51-96 |
