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 erstem Autor
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| , , , , , , , , , , , , , , , und , Explaining Uncertainty in Multiple Sclerosis Lesion Segmentation Beyond Prediction Errors, 2025 |
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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 |
[DOI] |
| , Medical Image Retrieval: Applications and Resources, in: International Conference on Multimedia Retrieval, 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 , Combining object recognition, gaze tracking and electromyography to guide prosthetic hands: experiences from two reserch projects (2019), in: Jedlik Laboratory reports, VII:2 |
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| , , , , , , , , und , TransformEEG: Towards improving model generalizability in deep learning-based EEG Parkinson’s disease detection (2025), in: Elsevier |
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| und , KI-gestütztes Videomonitoring aquatischer Ökosysteme - Chancen, Grenzen und Praxisanforderungen (2025), in: Springer Pfofessional, Wasser Wirtschaft, 115(54-61) |
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| , und , Experiences from the ImageCLEF Medical Retrieval and Annotation Tasks, Springer, 2019 |
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| und , EaaS – Evaluation-as-a-Service in scientific challenges, Springer, 2019 |
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| , , , , , , und , LLM-Enhanced Information Mining for Medical Visual Question Answering, Workshop on Large Language Model Using Multi-modal Data for User Modeling, Sydney, Australia, 2025. (2025), in: WWW '25: The ACM Web Conference 2025(2297-2305) |
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| und , Automated segmentation of pediatric neuroblastoma on multi-modal (2025), in: MDPI Bioengineering |
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| und , Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge (2025), in: Arxiv |
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| , , , und , BONBID-HIE 2023: Lesion Segmentation Challenge in BOston Neonatal Brain Injury Data for Hypoxic Ischemic Encephalopathy (2025), in: Publimed |
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| , , , , , , und , Identification and retrieval of prostate cancer cases using a content-based search tool (2019), in: Pathology Informatics |
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| , , , , und , A systematic comparison of deep learning strategies for weakly supervised Gleason grading,, in: SPIE Medical Imaging, Houstonm, TX, USA, 2020 |
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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 , Combining weak and strong supervised learning improves strong supervision in Gleason pattern classification (2021), in: BMC Medical Imaging |
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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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| , , , und , An Augmented Reality Environment to Provide Visual Feedback to Amputees during sEMG Data Acquisitions, in: TAROS (Towards Autonomous Robotic Systems), London, United Kingdom, 2019 |
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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 ImageCLEFmed 2020 Concept Prediction Task: Medical Image Understanding, in: CLEF working notes, CEUR, 2020 |
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, , , , , und , Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography (2019), in: Computer Methods and Programs in Biomedicine
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| , , und , Comparing Baseline and Day-1 Diffusion MRI Using Multimodal Deep Embeddings for Stroke Outcome Prediction, in: 2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI), IEEE, Seiten 1--5, 2026 |
| , , , , , , , , , , und , The consensus-based CINEX guideline for reporting clinical information extraction studies (2026), in: Journal of the American Medical Informatics Association(ocag136) |
| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , und , Common limitations of performance metrics in biomedical image analysis, in: MIDLconference, 2021 |
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| , , , , , , , , , und , BIAS: Transparent reporting of biomedical image analysis challenges (2020), in: Medical Image Analysis |
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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 , A comparative study of deep convolutional neural networks for the analysis of retinal damage in optical coherence tomography, in: Imaging Informatics for Healthcare, Research, and Applications, 2024 |
[DOI] |
| , , und , Automated OCT B-Scan Classification of Macular Scan Volumes with DINOv2 and Multi-Head Attention (2025), in: Investigative Ophthalmology & Visual Science June 2025, Vol.66, 5440., vol. 66 |
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| , , , , , , , , und , Gaze, behavioral, and clinical data for phantom limbs after hand amputation from 15 amputees and 29 controls (2020), in: Scientific Data, 7:1(1--14) |
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| , , , und , Deep learning based retrieval system for gigapixel histopathology cases and open access literature (2019), in: Pathology Informatics |
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, , , , und , A Quantitative Taxonomy of Human Hand Grasps, (2019), in: Journal of NeuroEngineering and Rehabilitation,, 16:28
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| , , , und , Automating quality control for standardized structured radiology reports using text analysis, in: ECR 2020, Vienna, Austria, 2020 |
| , , , und , An End-to-End 3D ConvLSTM-based Framework for Early Diagnosis of Alzheimer's Disease from Full-Resolution Whole-Brain sMRI Scans, in: Computer-Based Medical Systems, 2021 |
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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 , 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 , 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 , Automatic Multi-structure Segmentation in Cone Beam Computed Tomography Volumes Using Deep Encoder-Decoder Architectures (2025), in: Supervised and Semi-supervised Multi-structure Segmentation and Landmark Detection in Dental Data (MICCAI 2024), 15571(63-71) |
[DOI] [URL] |
| und , 3-D Image-to-Image Fusion in Lightsheet Microscopy by Two-Step Adversarial Network: Contribution to the Fusemycells Challenge (2025), in: 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI) |
[DOI] [URL] |
und , DeepHistReg: Unsupervised Deep Learning Registration Framework for Differently Stained Histology Samples (2021), in: Computer Methods and Programs in Biomedicine
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| und , InvNet: A Deep Learning Approach to Invert Complex Deformation Fields, in: ISBI, Nice, France, 2021 |
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| und , Learning-Based Affine Registration of Histological Images, in: International Workshop on Biomedical Image Registration, Springer, Seiten 12--22, 2020 |
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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 , The TopCoW Challenge—Topology-Aware Circle of Willis Segmentation for CT and MR Angiography (2026), in: NEJM AI, 3:8(AIdbp2500994) |
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| , , , , , , , und , Improving sEMG signals recognition accuracy using 3D compression and broad learning system (2025), in: SIGNAL, IMAGE AND VIDEO PROCESSING, 19:13 |
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| , , , , , , 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 , EEG-To-fMRI Cross-Modal Prediction via Hierarchical Temporal Attention Network: Toward Accessible Functional Neuroimaging (2026), in: Concurrency and Computation: Practice and Experience, 38:14(e70860) |
| , , , und , A Ni/CCF@PDMS-based flexible and electromagnetic interference-shielding surface electromyography/electrooculography sensor (2026), in: SENSORS AND ACTUATORS. A, PHYSICAL, 400(117530--117530) |
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| , , , , 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 , Standardized quantitative radiomics for high-throughput image-based phenotyping (2020), in: Radiology, 295:2(328-338)
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