Schlagworte:
- https://publications.hevs.ch/index.php/keywords/single/197
- (Bio-) Medical Image Registration
- 3D Network
- 3D Segme
- 3D U-Net Author Keywords Segmentation
- 3D-color-grayscale conversion
- Active Learning
- adversarial learning
- algorithms
- Annotation Process
- Annotations
- Aorta segmentation
- AortaSeg24
- Automatic weak labels
- Benchmark testing
- Biodiversity Machine Learning AI Species Individual Identification Prediction Species Distribution Modeling
- Biological signal acquisition
- BLS
- Brain
- Challenge Participants
- Chopped carbon fiber
- citation screening
- Classification
- Clinical decision support
- Common Benchmark
- Computed Tomography Angiography
- computed tomography MeSH Terms Cone-Beam Computed Tomography
- computer vision
- Cone Beam Computed Tomography Deep Learning Image Segmentation ToothFairy
- Cone-beam Computed Tomography Images
- Cone-beam Computed Tomography Volume
- Cortical lesions
- Data Augmentation
- Data Challenges
- Data Challenges · (Bio-) Medical Image Registration
- Deep Learning
- Deep Neural Network
- detection
- Dice Loss
- Dice Similarity Coefficient
- DiffusionModel
- digital pathology
- Domain Dataset
- EEE Keywords Three-dimensional displays
- Electromagnetic interference shielding
- Explainable AI
- explainable AI (XAI)
- Final Ranking
- Flexible sensor
- Focal Loss
- Fuse-MyCells
- Generative Adversarial Networks
- generative models
- Histopathology
- Histopathology image classification
- human-machine interaction
- Humans
- Image Captioning
- image retrieval
- image segmentation
- Image Understanding
- Image-to-Image
- ImageCLEF
- ImageCLEF benchmarking lab
- Imaging
- Inferior Alveolar Canal
- Inferior Alveolar Nerve
- Information extraction
- interpretability
- Intersection Over Union
- Irrigation
- Knowledge Injection
- large language model
- Large Language Models
- Lightsheet Microscopy
- Lung graph model
- machine learning
- Magnetic resonance imaging
- Mandible
- Mandibular Canal
- Medical Experts
- Medical image processing Medical image caption analysis Medical concept prediction Visual question answering Generative Adversarial Networks Synthetic Data Generation Image Segmentation Pictogram communication Multilingual Image Retrieval ImageCLEF
- medical imaging
- medical synthetic data
- Medical Visual Question Answering
- Mental Foramen
- MOT
- Multi-label classification
- multi-task learning
- Multimodal Learning
- Multiple sclerosis
- Multiview Fusion
- Natural Language Processing
- Neural Network
- Noisy labels
- Panoramic Radiographs
- Private Dataset
- Proposals Index Terms Inferior Alveolar
- Public Datasets
- Pulmonary embolism
- Pulmonary hypertension
- Radiology
- Radiology Report Generation
- retrospective matching network
- Scanning electron microscopy
- segmentation
- SEMG
- semi-supervised learning
- Sparse Labeling
- Statistical Shape Model
- Surgery
- Systematic Review
- systematic reviews
- Teeth
- texture analysis
- Three-Dimensional
- tooth
- training
- Training Data
- Training Set
- Trustworthy AI
- UAV
- whole slide imaging
- wrong labels
- X-ray imaging
Publikationen von Henning Müller sortiert nach Titel
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| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , und , Multi-class segmentation of aortic branches and zones in computed tomography angiography: the aortaseg24 challenge (2026), in: Medical Image Analysis(104188) |
| , , , , , , , , , , , und , Multi-Method eXplainability for Similarity Assessment in AI-Denoised PET Imaging, in: 2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI), Seiten 1-4, 2026 |
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| , , , , , , , und , Multi-Scale Multiple Instance Learning for the Classification of Digital Pathology Images with Global Annotations, in: COMPAY workshop at MICCAI, 2021 |
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| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , und , Multi-structure segmentation in CBCT volumes: The ToothFairy2 challenge (2026), in: Medical Image Analysis(104095) |
| , , , , , , , , , , , , , , , , , , , , , , , , , , und , Multimedia Retrieval in Medical, Social Media and Content Recommendation Applications ECIR 2025 (2025), in: Lecture Notes in Computer Science, vol 16089. Springer, Cham., 16089(290-314) |
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| , , , und , Multimodal Latent Semantic Alignment for Automated Prostate Tissue Classification and Retrieval, in: MICCAI, Lima, Peru, 2020 |
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| , , , , , , und , Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images (2021), in: Frontiers in Computer Science |
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| , , , und , 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) |
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| , und , One Model to Magnify Them All: Efficient Scale-Invariant Histopathology via Conditional Normalization and Continuous Magnification Training (2026), in: arXiv preprint arXiv:2608.09403 |
| , , , , , , und , Optimization-enhanced Gaussian mixture modeling for data-driven subphenotyping of septic shock (2026), in: Computer Methods in Biomechanics and Biomedical Engineering(1--14) |
| , , , , , , , , , , , , , , und , Overview of ImageCLEFmedical 2025 – Medical Concept Detection and Interpretable Caption Generation (2025), in: CEUR-WS.org/Vol-4038 - CLEF 2025 Working Notes, 4038:Paper 170 |
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| , , , , , , und , Overview of ImageCLEFMedical 2025 GANs Task: Training Data Analysis and Fingerprint Detection (2025), in: CLEF 2025 Working Notes, 9 – 12 September 2025, Madrid, Spain / coeur-ws.org, 4038(paper 169) |
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| , , , , und , Overview of ImageCLEFtuberculosis 2020: Automatic CT-based report generation, in: CLEF working notes, CEUR, 2020 |
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| , , , und , Overview of ImageCLEFtuberculosis 2021 - CT-based Tuberculosis Type Classification, in: CLEF working notes, 2021 |
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| , , , , , , , , , , , , , , , , und , Overview of LifeCLEF 2020: a System-oriented Evaluation of Automated Species Identification and Species Distribution Prediction, Thessaloniki, Greece, Springer LNCS, 2020 |
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| , , , , , , , , , , , , , , , , , und , Overview of LifeCLEF 2021: an Evaluation of Machine-Learning based Species Identification and Species Distribution Prediction, in: CLEF proceedings, Springer, 2021 |
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| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , und , Overview of the ImageCLEF 2020: Multimedia Retrieval in Medical Lifelogging, Nature and Internet Applications, in: CLEF Proceedings, 2020 |
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| , , und , Overview of the ImageCLEFmed 2020 Concept Prediction Task: Medical Image Understanding, in: CLEF working notes, CEUR, 2020 |
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| , , , , und , Overview of the ImageCLEFmed 2021 Concept & Caption Prediction Task, in: CLEF working notes, 2021 |
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| , , , und , Overview of the VQA-Med Task at ImageCLEF 2020: Visual Question Answering and Generation in the Medical Domain, in: CLEF workingnotes 2020, Thessaloniki Greece,, 2020 |
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| , , , und , Overview of the VQA-Med Task at ImageCLEF 2021: Visual Question Answering and Generation in the Medical Domain, in: CLEF working notes, 2021 |
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| , , , , , , , , , und , PROCESS Data Infrastructure and Data Service (2020), in: Computing and Informatics |
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| , , , , und , Prospects and Challenges of Radiomics by Using Nononcologic Routine Chest CT, in: Radiology: Cardiothoracic Imaging, 2020 |
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| , , , , und , Questioning Domain Adaptation in Myoelectric Hand Prostheses Control: An Inter-and Intra-Subject Study (2021), in: Sensors, 21:22(7500) |
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, , , , , und , Radiomics Analysis Using The Image Biomarker Standardization Initiative (IBSI) Benchmarks And Guidelines, in: Radiological Society of North America (RSNA) 2021 Annual Meeting, 2021
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| , , , , , , , , , und , Reference Exascale Architecture - Extended Version (2020), in: Computer And Informatics |
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| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , und , Reporting checklist for foundation and large language models in medical research (REFINE): an international consensus guideline (2026), in: Diagnostic and Interventional Radiology |
| , , , , und , Retrospective Matching Network-Based One-Shot Multi-Object Tracking Method for UAV (2026), in: CONCURRENCY AND COMPUTATION, 38:1 |
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| , , , , , , , , und , Revealing most suitable CT radiomics features for retrospective studies with heterogeneous datasets, in: European Congress of Radiology (ECR) 2021, ONLINE edition, 2021 |
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| , , , , , , , und , RoBuster—Corpus Annotated With Risk of Bias Text Spans in Randomized Controlled Trials in Physiotherapy and Rehabilitation: Corpus Development and Annotation Study (2026), in: JMIR formative research, 10:1(e55127) |
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| , , , , , , und , Segmenting the Inferior Alveolar Canal in CBCTs Volumes: The ToothFairy Challenge (2025), in: IEEE Transactions on Medical Imaging, Volume 44, Issue : 4, Arpil 2025(1890-1906) |
[DOI] [URL] |
| , , und , Semi-supervised learning with a teacher-student paradigm for histopathology classification: a resource to face data heterogeneity and lack of local annotations, in: Workshop Artificial Intelligence for Digital Pathology, ICPR, Milano, Italy, 2021 |
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| , , und , Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations: an experiment on histopathology image classification (2021), in: Medical Image Analysis |
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| , , und , Semi-Weakly Supervised Learning for Prostate Cancer Image Classification with Teacher-Student Deep Convolutional Networks, in: MICCAI workshop Labels, Lima, Peru, 2020 |
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| , , , , und , Sharpening Local Interpretable Model-agnostic Explanations for Histopathology: Improved Understandability and Reliability, in: MICCAI 2021, Springer, 2021 |
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| , , , , , , und , Standardised convolutional filtering for radiomics, 2020 |
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, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , und , Standardized quantitative radiomics for high-throughput image-based phenotyping (2020), in: Radiology, 295:2(328-338)
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| , , , , , , , , und , Structural-based uncertainty in deep learning across anatomical scales: Analysis in white matter lesion segmentation (2025), in: Computers in Biology and Medicine |
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| , , , , , , , , , , , , , , , , , , , , , , , , , , , , und , The 2021 ImageCLEF Benchmark: Multimedia Retrieval in Medical, Nature, Internet and Social Media Applications, in: European Conference on Information Retrieval, Springer, 2021 |
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| , , , , , , , , , , und , The consensus-based CINEX guideline for reporting clinical information extraction studies (2026), in: Journal of the American Medical Informatics Association(ocag136) |
, , , , , , , , und , 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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, , , , und , The Importance of Feature Aggregation in Radiomics: A Head and Neck Cancer Study (2020), in: Nature Scientific Reports, 10:19679
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| , , , , , , , , , , und , The TopCoW Challenge—Topology-Aware Circle of Willis Segmentation for CT and MR Angiography (2026), in: NEJM AI, 3:8(AIdbp2500994) |
| , , , , und , Training a deep neural network for small and highly heterogeneous MRID datasets for cancer grading, in: EMBC Conference, IEEE, 2020 |
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| , , , , und , Training Deep Neural Networks for Small and Highly Heterogeneous MRI Datasets for Cancer Grading, in: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC), IEEE, Seiten 1758--1761, 2020 |
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| , , , , , , , , und , TransformEEG: Towards Improving Model Generalizability in Deep Learning-based EEG Parkinson's Disease Detection (2025), in: arXiv preprint arXiv:2507.07622 |
| , , , , , , , , und , TransformEEG: Towards improving model generalizability in deep learning-based EEG Parkinson’s disease detection (2025), in: Elsevier |
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| und , Unsupervised Learning-based non-rigid registration of high resolution histology images, in: MICCAI workshop on Machine Learning in Medical Imaging, Springer, 2020 |
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| , , , , und , Using Publicly Available Medical Images from the Open Access Literature and Social Networks for Model Training and Knowledge Extraction, in: Multimedia Modeling (MMM 2020), Seoul, Korea, 2020 |
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| , , und , Variability of Muscle Synergies in Hand Grasps: Analysis of Intra-and Inter-Session Data (2020), in: Sensors, 20:15(4297) |
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