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 , Concept attribution: Explaining CNN decisions to physicians (2020), in: Computers in Biology and Medicine |
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| , und , Interpreting intentionally flawed models with linear probes, in: ICCV workshop on statistical deep learning in computer vision, Seoul, Korea, 2019 |
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| , , und , Guiding CNNs towards Relevant Concepts by Multi-task and Adversarial Learning, arxiv, 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 , Learn2Reg 2024: New Benchmark Datasets Driving Progress on New Challenges (2025), in: Hansen et al. 2025 / Melba Journal - Machine Learning for Biomedical Imaging, Volume 3 |
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| , , , , , , , , und , Learn2Reg 2024: New Benchmark Datasets Driving Progress on New Challenges (2025), in: Melba, Volume 3(775-791) |
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| , , , , , , , , , , , , , , , , , , , , , , und , Heterogeneous exascale computing, in: INES 2018 conference, Springer, 2019 |
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| , , , , , , und , An Exploration of Uncertainty Information for Segmentation Quality Assessment, in: SPIE Medical Imaging 2020: Image Processing, Houston, TX, USA, Seiten 381-390, SPIE, 2020 |
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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 , 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 , ImageCLEF 2019: Multimedia Retrieval in Medical Nature, Security and Lifelogging Applications, in: ECIR 2019, Cologne, Germany, 2019 |
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| , und , ImageCLEF 2020: Multimedia Retrieval in Lifelogging, Medical, Nature, and Internet Applications, in: ECIR 2020, Lisbon Portugal, Springer LNCS, 2020, 2020 |
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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 , 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 , ImageCLEF 2019: Multimedia Retrieval in Lifelogging, Medical, Nature and Security Applications, in: CLEF conference Proceedings, Lugano, Switzerland, Springer LNCS, 2019 |
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| , , , , , , , , , und , Imageclef 2026: Multimodal challenges in medicine, science, agritech, and security, in: European Conference on Information Retrieval, Springer, Seiten 336--344, 2026 |
| , , , , und , Benchmarking Image Retrieval Diversification Techniques for Social Media (2020), in: IEEE Transactions on Multimedia |
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, und , A large calibrated database of hand movements and grasp kinematics (2020), in: Scientific Data
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| , , , , und , Kinematic synergies of hand grasps, in: ESB 2019, Vienna, Austria, 2019 |
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| , , und , Kinematic synergies of hand grasps: a comprehensive study on a large publicly available dataset (2019), in: Journal of Neuroengineering and rehabilitation |
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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 , 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 , 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 |
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, , , , , und , 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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| , , , , , , , und , Biodiversity information retrieval through content-based identification: a long-term evaluation, Springer, 2019 |
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| , , , , , , , und , LifeCLEF 2019: Biodiversity Identification and Prediction Challenges, in: ECIR 2019, Cologne, Germany, 2019 |
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| , , , , , , , , , , , , , , und , LifeCLEF 2020 Teaser: Biodiversity Identification and Prediction Challenges, in: ECIR 2020, Lisbon, Portugal, Springer LNCS, 2020 |
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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 , Challenges on Species Presence Prediction and Identification, and Individual Animal Identification, Springer Lecture Notes in Computer Science, Proceedings of CLEF 2025, Madrid, Spain, 2025. (2025), in: Lecture Notes in Computer Science, vol 15576. Springer, Cham, 15576(373-381) |
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, , und , Fusing Learned Representations from Riesz and Deep CNNs for Lung Tissue Classification (2019), in: Medical Image Analysis, 56(172-183)
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| , , , , , , , , und , CLEF proceedings, Springer, LNCS, 2021 |
| , , und , Generalizing Convolution Neural Networks on Stain Color Heterogeneous Data for Computational Pathology, in: SPIE Medical Imaging, Houston, TX, USA,, 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 ImageCLEFtuberculosis 2020: Automatic CT-based report generation, in: CLEF working notes, CEUR, 2020 |
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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 , An overview of self-supervised deep learning applications to molecular data (2026), in: Briefings in Bioinformatics, 27:4(bbag380) |
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| , , , , , , , und , A full pipeline to analyze lung histopathology images, in: Digital and Computational Pathology, SPIE Medical Imaging, 2024 |
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| , , , , , und , Diversity-Augmented Diffusion Network With LLM Assistance For Radiology Report Generation (2025), in: WWW '25: Companion Proceedings of the ACM on Web Conference 2025(2288-2296) |
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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) |
| , , , und , H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression, in: ICCV 2021 workshop on Computational Challenges in Digital Pathology, 2021 |
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| , , , , , , , , , , , , , und , Automatic labels are as effective as manual labels in digital pathology images classification with deep learning (2025), in: Journal of Pathology Informatics, 18(100462) |
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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 , 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 , Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images (2021), in: Frontiers in Computer Science |
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| , , , , , , , , , , , und , 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, 53:3(e70374) |
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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 , Biologically constrained Transformers improve stability and interpretability in single-cell transcriptomics, in: Proceedings of Bioinformatics Italian Society 2026, 2026 |
| , , , , , , , , , , , , , , , , , , und , A comparative study of deep learning for cortical lesion MRI segmentation with explainability analysis in multiple sclerosis (2026), in: NeuroImage: Clinical, 50(104007) |
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