Schlagworte:
- adversarial learning
- Automatic segmentation
- Challenge
- Classification
- Computer Vision and Pattern Recognition (cs.CV)
- concept vectors
- 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
Publikationen von Vincent Andrearczyk
| 1-50 | 51-86 |
2024
A Bispectral 3D UNet for Rotation Robustness in Medical Segmentation, in: The First Workshop on Topology- and Graph-Informed Imaging Informatics at MICCAI, 2024 | , , , , und ,
Automatic Detection and Multi-Component Segmentation of Brain Metastases in Longitudinal MRI (2024), in: Nature Scientic Reports | , , , , , , , , und ,
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 | , , , , , , , , , und ,
[DOI] |
Explainability in automatic Paramagnetic Rim Lesion classification, in: 40th Congress Of The European Committee For Treatment And Research In Multiple Sclerosis (ECTRIMS), 2024 | , , , , , , , , , , , und ,
|
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, 2024 | , , , , , , und ,
|
Instance-level explanations in multiple sclerosis lesion segmentation: a novel localized saliency map, in: ISMRM 2024, 2024 | , , , , , , , und ,
Instance-level quantitative saliency in multiple sclerosis lesion segmentation (2024), in: arxiv | , , , , , , , und ,
[URL] |
The image biomarker standardization initiative: Standardized convolutional filters for reproducible radiomics and enhanced clinical insights (2024), in: Radiology, 310:2
|
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , und ,
[DOI] |
2023
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)
|
, , , , , , , , , , und ,
[URL] |
Concept discovery and Dataset exploration with Singular Value Decomposition, ICLR Workshop on Trustworthy ML, 2023 | , , , und ,
|
Deep learning interpretability: measuring the relevance of clinical concepts in convolutional neural networks features, Elsevier, Band 157-192, 2023 | , und ,
|
Disentangling Neuron Representations with Concept Vectors, in: Proceedings of the 2nd Explainable AI for Computer Vision (XAI4CV) Workshop at CVPR 2023, 2023 | , , und ,
|
Explanation Generation via Decompositional Rules Extraction for Head and Neck Cancer Classification, in: Explainable and Transparent AI and Multi-Agent Systems, 2023 | , , , , und ,
|
FLAIR vs MPRAGE contribution to white matter lesion automatic segmentation in MS using localized saliency maps, in: Bern Interpretable AI Symposium (BIAS), 2023 | , , , , , , , und ,
|
Head and Neck Tumor Segmentation and Outcome Prediction, Springer International Publishing, 2023 | , , und ,
[DOI] [URL] |
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 | , , , , , , und ,
|
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 | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , und ,
|
Learning Interpretable Microscopic Features of Tumor by Multi-task Adversarial CNNs To Improve Generalization (2023), in: Machine Learning for Biomedical Imaging (MELBA), 2 | , , , und ,
[DOI] |
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision, 2023 | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , und ,
[URL] |
Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT, Seiten 1-30, Springer, Cham, 2023 | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , und ,
[DOI] [URL] |
Why is the winner the best?, in: CVPR, 2023 | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , und ,
[URL] |
Wide kernels and their DCT compression in convolutional networks for nuclei segmentation (2023), in: Informatics in Medicine Unlocked, 43(101403) | , und ,
[DOI] [URL] |
2022
A Global Taxonomy of Interpretable AI: Unifying the Terminology for the Technical and Social Sciences (2022), in: Artificial Intelligence Review, 56(3473–3504) | , , , , , , , , , , , , , , und ,
|
Automated Tumor Segmentation in Radiotherapy (2022), in: Seminars in Radiation Oncology, 32:4(319-329) | , , , , und ,
[DOI] [URL] |
Biomedical image analysis competitions: The state of current participation practice (2022), in: arXiv | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , und ,
[DOI] [URL] |
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)
|
, , , , , , , , , und ,
|
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, Seiten 367–380, Springer International Publishing, 2022 | , , , , , , , und ,
[DOI] |
Deep Learning Interpretability: Measureing the relevance of clinical concepts in CNN features, in: State of the art in neural networks, Elsevier, 2022 | , und ,
Head and Neck Tumor Segmentation and Outcome Prediction, Springer International Publishing, 2022 |
[DOI] [URL] |
HEad and neCK TumOR segmentation and outcome prediction: The HECKTOR challenge, in: European Society of Radiology, 2022 | , , , , , , , und ,
|
Head and Neck Tumor Segmentation in PET/CT: The HECKTOR Challenge (2022), in: Medical Image Analysis, 77(102336)
|
, , , , , , , , , , , , , , , , , , , , , , , , , und ,
[URL] |
Multi-Organ Nucleus Segmentation Using a Locally Rotation Invariant Bispectral U-Net, in: Medical Imaging with Deep Learning, 2022 | , , , und ,
[URL] |
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, Seiten 1-37, 2022 | , , , , , , , , , und ,
[DOI] [URL] |
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), Seiten 4731-4735, 2022 | , , , , und ,
|
The Image Biomarker Standardisation Initiative (IBSI) On Reproducible Convolutional Radiomics, in: European Society of Radiology, 2022 | , , , , , und ,
|
2021
Evaluation and Comparison of CNN Visual Explanations for Histopathology, in: XAI-AAAI-21, 2021 | , , und ,
|
Fully Automatic Head and Neck Cancer Prognosis Prediction in PET/CT, in: Multimodal Learning for Clinical Decision Support, Seiten 59-68, Springer LNCS, 2021 | , , , , , und ,
|
Head and Neck Tumor Segmentation, Springer International Publishing, 2021 |
[URL] |
Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods (2021), in: Journal of Personalized Medicine, 11:9 | , , , , , , , und ,
[URL] |
Multi-Task Deep Segmentation and Radiomics for Automatic Prognosis in Head and Neck Cancer, in: 4th Workshop on PRedictive Intelligence in MEdicine, Seiten 147-156, Springer LNCS, 2021 | , , , , , und ,
[URL] |
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) | , , , und ,
|
Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT, Seiten 1-21, 2021 | , , , , , , und ,
|
Radiomics Analysis Using The Image Biomarker Standardization Initiative (IBSI) Benchmarks And Guidelines, in: Radiological Society of North America (RSNA) 2021 Annual Meeting, 2021
|
, , , , , und ,
|
Sharpening Local Interpretable Model-agnostic Explanations for Histopathology: Improved Understandability and Reliability, in: MICCAI 2021, Springer, 2021 | , , , , und ,
|
2020
3D Solid Spherical Bispectrum CNNs for Biomedical Texture Analysis, 2020
|
, , , und ,
[URL] |
| 1-50 | 51-86 |