Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
Published:
This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.
Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
projects
BigPicture
The goal of the BigPicture project is to create the largest repository of digital pathology data in Europe.
DeepImplant
The DeepImplant project aims to propose a ready-to-use system for automatic modeling of personalized cranial implants.
ExaMode
The goal of the ExaMode project is to propose scalable methods for multimodal analysis of exascale heterogeneous data.
DiagNeuro
The DiagNeuro project aims at early neurodegenerative diseases diagnosis using multimodal signals acquired in augmented and mixed reality.
publications
Improving oncoplastic breast tumor bed localization for radiotherapy planning using image registration algorithms
Published in Physics in Medicine & Biology, 2018
Article presenting usage of image registration method for improving oncoplastic breast tumor bed localization.
Recommended citation: Marek Wodzinski, et al., Improving oncoplastic breast tumor bed localization for radiotherapy planning using image registration algorithms, Physics in Medicine & Biology, Vol. 63, 2021. https://iopscience.iop.org/article/10.1088/1361-6560/aaa4b1
Deep Learning Approach to Parkinson’s Disease Detection Using Voice Recordings and Convolutional Neural Network Dedicated to Image Classification
Published in IEEE EMBC 2019, 2019
Deep learning-based method to classify Parkinson’s disease using CNNs dedicated to image classification.
Recommended citation: Marek Wodzinski, et al. Deep learning approach to Parkinson’s disease detection using voice recordings and convolutional neural network dedicated to image classification, IEEE EMBC 2019, 2019. https://ieeexplore.ieee.org/document/8856972
Convolutional Neural Network Approach to Classify Skin Lesions Using Reflectance Confocal Microscopy
Published in IEEE EMBC 2019, 2019
Article presenting a method for learning-based classification of skin lesions using RCM images.
Recommended citation: Marek Wodzinski, et al., Convolutional Neural Network Approach to Classify Skin Lesions Using Reflectance Confocal Microscopy, IEEE EMBC 2019, 2019. https://ieeexplore.ieee.org/document/8856731
ANHIR: automatic non-rigid histological image registration challenge
Published in IEEE Transactions on Medical Imaging, 2020
Article presenting the outcomes of the ANHIR challenge organzied during IEEE ISBI 2019 conference.
Recommended citation: Jiří Borovec, et al., ANHIR: automatic non-rigid histological image registration challenge, IEEE Transactions on Medical Imaging, Vol. 39, 2020. https://ieeexplore.ieee.org/document/9058666
Learning-Based Affine Registration of Histological Images
Published in WBIR 2020, 2020
Article presenting learning-based affine registration method to align WSIs.
Recommended citation: Marek Wodzinski, Henning Müller, Learning-Based Affine Registration of Histological Images, WBIR 2020, 2020. https://link.springer.com/chapter/10.1007/978-3-030-50120-4_2
Automatic Quality Assessment of Reflectance Confocal Microscopy Mosaics using Attention-Based Deep Neural Network
Published in IEEE EMBC 2020, 2020
Prelimiary work presenting the possibility to evaluate quality of RCM mosaics using attention-based neural networks.
Recommended citation: Marek Wodzinski, et al., Automatic Quality Assessment of Reflectance Confocal Microscopy Mosaics using Attention-Based Deep Neural Network, IEEE EMBC 2020, 2020. https://ieeexplore.ieee.org/document/9176557'
Unsupervised Learning-Based Nonrigid Registration of High Resolution Histology Images
Published in MICCAI MLMI 2020, 2020
Deep learning-based method to register high-resolution histology images.
Recommended citation: Marek Wodzinski, Henning Müller, Unsupervised Learning-Based Nonrigid Registration of High Resolution Histology Images, MICCAI MLMI, 2020. https://link.springer.com/chapter/10.1007/978-3-030-59861-7_49
DeepHistReg: Unsupervised Deep Learning Registration Framework for Differently Stained Histology Samples
Published in Computer Methods and Programs in Biomedicine, 2021
Learning-based method for deformable registration of WSIs acquired using different stains.
Recommended citation: Marek Wodzinski, Henning Müller, DeepHistReg: Unsupervised Deep Learning Registration Framework for Differently Stained Histology Samples, Computer Methods and Programs in Biomedicine, Vol. 198, 2021. https://www.sciencedirect.com/science/article/pii/S0169260720316321
Multistep, automatic and nonrigid image registration method for histology samples acquired using multiple stains
Published in Physics in Medicine & Biology, 2021
Article presenting an automatic algorithm for WSI registration that scored 3rd place during the ANHIR challenge.
Recommended citation: Marek Wodzinski, Andrzej Skalski, Multistep, automatic and nonrigid image registration method for histology samples acquired using multiple stains, Physics in Medicine & Biology, Vol 66., 2021. https://iopscience.iop.org/article/10.1088/1361-6560/abcad7
Automatic classification of canine thoracic radiographs using deep learning
Published in Scientific Reports, 2021
AI-based method to classify canine thoracic radiographs.
Recommended citation: Tommaso Banzato, Marek Wodzinski, Silvia Burti, et al., Automatic classification of canine thoracic radiographs using deep learning, Scientific Reports, Vol 11, 2021. https://www.nature.com/articles/s41598-021-83515-3
Learning-based local quality assessment of reflectance confocal microscopy images for dermatology applications
Published in Biocybernetics and Biomedical Engineering, 2021
Article presenting a learning-based method for local quality assessment of RCM images.
Recommended citation: Miroslawa Sikorska, Andrzej Skalski, Marek Wodzinski, et al., Learning-based local quality assessment of reflectance confocal microscopy images for dermatology applications, Biocybernetics and Biomedical Engineering, Vol 41, 2021. https://www.sciencedirect.com/science/article/pii/S0208521621000632
Semi-Supervised Deep Learning-Based Image Registration Method with Volume Penalty for Real-Time Breast Tumor Bed Localization
Published in Sensors, 2021
Article introducing a volume-penalty method to handle missing data during breast tumor bed localization using image registration.
Recommended citation: Marek Wodzinski, et al., Semi-Supervised Deep Learning-Based Image Registration Method with Volume Penalty for Real-Time Breast Tumor Bed Localization, Sensors, 2021. https://www.mdpi.com/1424-8220/21/12/4085
Adversarial Affine Registration for Real-Time Intraoperative Registration of 3-D US-US for Brain Shift Correction
Published in MICCAI-ASMUS 2021, 2021
Method presenting a 3-D US-US affine registration for brain shift correction using adversarial training.
Recommended citation: Marek Wodzinski, Andrzej Skalski, Adversarial Affine Registration for Real-Time Intraoperative Registration of 3-D US-US for Brain Shift Correction, MICCAI ASMUS, 2021. https://link.springer.com/chapter/10.1007/978-3-030-87583-1_8
An AI-based algorithm for the automatic classification of thoracic radiographs in cats
Published in Frontiers in Veterinary Science, 2021
Article presenting an AI-based methods to classify thoracic radiographs in cats.
Recommended citation: Tommaso Banzato, Marek Wodzinski, Federico Tauceri, et al., An AI-based algorithm for the automatic classification of thoracic radiographs in cats, Frontiers in Veterinary Science, Vol. 8, 2021. https://www.frontiersin.org/articles/10.3389/fvets.2021.731936/full
Improving the Automatic Cranial Implant Design in Cranioplasty by Linking Different Datasets
Published in MICCAI 2021, 2021
Contribution to the AutoImplant challenge organized during MICCAI 2021 (1st place in all challenge tasks).
Recommended citation: Marek Wodzinski, Mateusz Daniol, Daria Hemmerling, Improving the Automatic Cranial Implant Design in Cranioplasty by Linking Different Datasets, MICCAI Cranial Implant Design Challenge, 2021. https://link.springer.com/chapter/10.1007/978-3-030-92652-6_4
Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations
Published in npj Digital Medicine, 2022
Article presenting a method how to train deep neural networks on digital pathology data without using human annotations.
Recommended citation: Niccolò Marini, et al., Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations, npj Digital Medicine, 2022. https://www.nature.com/articles/s41746-022-00635-4
Deep Learning-based Framework for Automatic Cranial Defect Reconstruction and Implant Modeling
Published in Computer Methods and Programs in Biomedicine, 2022
Article presenting a pipeline dedicated to automatic cranial implant design and verification in mixed reality.
Recommended citation: Marek Wodzinski, et al., Deep Learning-based Framework for Automatic Cranial Defect Reconstruction and Implant Modeling, Computer Methods and Programs in Biomedicine, Vol. 226, 2022. https://www.sciencedirect.com/science/article/pii/S0169260722005545
Learn2Reg: Comprehensive Multi-Task Medical Image Registration Challenge, Dataset and Evaluation in the Era of Deep Learning
Published in IEEE Transactions on Medical Imaging, 2022
Article presenting the outcomes of the Learn2Reg challenge (medical image registration in radiology).
Recommended citation: Alessa Hering, et al., Learn2Reg: Comprehensive Multi-Task Medical Image Registration Challenge, Dataset and Evaluation in the Era of Deep Learning, IEEE Transactions on Medical Imaging, Vol. 42, 2023. https://ieeexplore.ieee.org/document/9925717
Data-driven color augmentation for H&E stained images in computational pathology
Published in Journal of Pathology Informatics, 2023
Article describing robust and accurate method for intensity augmentation of H&E WSIs.
Recommended citation: Niccolò Marini, Sebastian Otalora, Marek Wodzinski, et al., Data-driven color augmentation for H&E stained images in computational pathology, Journal of Pathology Informatics, 2023. https://www.sciencedirect.com/science/article/pii/S2153353922007830
DRU-Net: Pulmonary Artery Segmentation via Dense Residual U-Network with Hybrid Loss Function
Published in Sensors, 2023
AI-based method to segment pulmonary artery tree from computed tomography volumes.
Recommended citation: Manahil Zulfiqar, Maciej Stanuch, Marek Wodzinski, Andrzej Skalski, DRU-Net: Pulmonary Artery Segmentation via Dense Residual U-Network with Hybrid Loss Function, Sensors, 2023. https://www.mdpi.com/1424-8220/23/12/5427
Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the autoimplant 2021 cranial implant design challenge
Published in Medical Image Analysis, 2023
Benchmark of all the methods proposed for the AutoImplant challenge organized during MICCAI 2021
Recommended citation: Jianning Li, et al., Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the autoimplant 2021 cranial implant design challenge, Medical Image Analysis, Vol. 88, 2023. https://www.sciencedirect.com/science/article/pii/S1361841523001251
Unsupervised Method for Intra-patient Registration of Brain Magnetic Resonance Images Based on Objective Function Weighting by Inverse Consistency: Contribution to the BraTS-Reg Challenge
Published in MICCAI - BrainLes 2022, 2023
A successful algorithm to MR volumes registration acquired before and after brain tumor resection.
Recommended citation: Marek Wodzinski, et. al., Unsupervised Method for Intra-patient Registration of Brain Magnetic Resonance Images Based on Objective Function Weighting by Inverse Consistency: Contribution to the BraTS-Reg Challenge, MICCAI BrainLes 2022, 2023. https://link.springer.com/chapter/10.1007/978-3-031-33842-7_21
Artifact Augmentation for Learning-based Quality Control of Whole Slide Images
Published in IEEE EMBC 2023, 2023
Method to automatically improve detection of WSI artifacts by smart augmentation.
Recommended citation: Artur Jurgas, Marek Wodzinski, Weronika Celniak, et. al., Artifact Augmentation for Learning-based Quality Control of Whole Slide Images, IEEE EMBC 2023, 2023. https://ieeexplore.ieee.org/abstract/document/10340997
Vision Transformer for Parkinson’s Disease Classification using Multilingual Sustained Vowel Recordings
Published in IEEE EMBC 2023, 2023
Transformed-based method to classify Parkinson’s disease based on vowel recordings.
Recommended citation: Daria Hemmerling, Marek Wodzinski, Juan Rafael Orozco-Arroyave, et. al., Vision Transformer for Parkinson’s Disease Classification using Multilingual Sustained Vowel Recordings, IEEE EMBC 2023, 2023. https://ieeexplore.ieee.org/abstract/document/10340478
Why is the winner the best?
Published in IEEE CVPR 2023, 2023
Article presenting the factors leading to winning scientific challenges in medical image analysis.
Recommended citation: Matthias Eisenmann, et al., Why is the winner the best?, IEEE CVPR 2023, 2023. https://ieeexplore.ieee.org/document/10203576
Robust Multiresolution and Multistain Background Segmentation in Whole Slide Images
Published in PCBBE 2023, 2023
Automatic segmentation of tissue from background in WSI images.
Recommended citation: Artur Jurgas, Marek Wodzinski, Manfredo Atzori, Henning Müller, Robust Multiresolution and Multistain Background Segmentation in Whole Slide Images, PCBBE 2023, 2023. https://link.springer.com/chapter/10.1007/978-3-031-38430-1_3
Biomedical image analysis competitions: The state of current participation practice
Published in arXiv, 2023
Article presenting the current state-of-the-art in biomedical image analysis competitions.
Recommended citation: Matthias Eisenmann, et al., Biomedical image analysis competitions: The state of current participation practice, arXiv:2212.08568, 2023. https://arxiv.org/abs/2212.08568
Development of an artificial intelligence-based method for the diagnosis of the severity of myxomatous mitral valve disease from canine chest radiographs
Published in Frontiers in Veterinary Science, 2023
Article presenting AI-based method to access severity of myxomatous mitral valbe disease.
Recommended citation: Carlotta Valente, Marek Wodzinski, Carlo Guglielmini, et. al., Development of an artificial intelligence-based method for the diagnosis of the severity of myxomatous mitral valve disease from canine chest radiographs, Frontiers in Veterinary Science, 2023. https://www.frontiersin.org/articles/10.3389/fvets.2023.1227009/full
High-Resolution Cranial Defect Reconstruction by Iterative, Low-Resolution, Point Cloud Completion Transformers
Published in MICCAI 2023, 2023
Transformer-based method to perform cranial defect reconstruction using point cloud representation.
Recommended citation: Marek Wodzinski, et. al., High-Resolution Cranial Defect Reconstruction by Iterative, Low-Resolution, Point Cloud Completion Transformers, MICCAI 2023, 2023. https://link.springer.com/chapter/10.1007/978-3-031-43996-4_32
An AI-based algorithm for the automatic evaluation of image quality in canine thoracic radiographs
Published in Scientific Reports, 2023
Desciription of an AI-based method to evaluate image quality in canine thoracic radiographs.
Recommended citation: Tommaso Banzato, Marek Wodzinski, Silvia Burti et al. An AI-based algorithm for the automatic evaluation of image quality in canine thoracic radiographs, Scientific Reports, Vol. 13, 2023. https://www.nature.com/articles/s41598-023-44089-4
Improving the classification of veterinary thoracic radiographs through inter-species and inter-pathology self-supervised pre-training of deep learning models
Published in Scientific Reports, 2023
Article describing a concept related inter-species and inter-pathology self-supervised pretraining.
Recommended citation: Weronika Celniak, Marek Wodzinski, Artur Jurgas, et. al., Improving the classification of veterinary thoracic radiographs through inter-species and inter-pathology self-supervised pre-training of deep learning models, Scientific Reports, Vol. 13, 2024. https://www.nature.com/articles/s41598-023-46345-z
Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA
Published in In Review - Medical Image Analysis, 2023
This article presents the outcomes of TopCoW challenge organized during MICCAI 2024 challenge.
Recommended citation: K. Yang, et. al., Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA, In Review, 2024. https://arxiv.org/abs/2312.17670
Deep Generative Networks for Heterogeneous Augmentation of Cranial Defects
Published in CVPR 2023, 2023
Preliminary results about the use of generative networks for data augmentation in the context of cranial defects reconstruction.
Recommended citation: Kamil Kwarciak, Marek Wodzinski, Deep Generative Networks for Heterogeneous Augmentation of Cranial Defects, IEEE CVPRW 2023, 2023. https://ieeexplore.ieee.org/document/10350616
Automatic Aorta Segmentation with Heavily Augmented, High-Resolution 3-D ResUNet: Contribution to the SEG.A Challenge
Published in MICCAI 2023, 2024
Contribution to the SEG.A Challenge organized during MICCAI 2023 (1st in clinical evaluation).
Recommended citation: Marek Wodzinski, Henning Müller, Automatic Aorta Segmentation with Heavily Augmented, High-Resolution 3-D ResUNet: Contribution to the SEG.A Challenge, MICCAI Challenge on Segmentation of the Aorta, 2024. https://link.springer.com/chapter/10.1007/978-3-031-53241-2_4
AI-Based Automated Custom Cranial Implant Design–Challenges and Opportunities with Case Study
Published in Manufacturing Conference, 2024
This conference paper presents a case-study related to design challenges in automatic cranial implant modeling.
Recommended citation: Mateusz Daniol, Daria Hemmerling, Marek Wodzinski, AI-Based Automated Custom Cranial Implant Design – Challenges and Opportunities with Case Study, Advances in Manufacturing IV, 2024. https://link.springer.com/chapter/10.1007/978-3-031-56456-7_6
RegWSI: Whole Slide Image Registration using Combined Deep Feature-and Intensity-Based Methods: Winner of the ACROBAT 2023 Challenge
Published in Computer Methods and Programs in Biomedicine, 2024
Article describing a novel WSI registration method that won the ACROBAT 2023 challenge.
Recommended citation: Marek Wodzinski et al. RegWSI: Whole slide image registration using combined deep feature- and intensity-based methods: Winner of the ACROBAT 2023 challenge, Computer Methods and Programs in Biomedicine, Vol. 250, 2024. https://www.sciencedirect.com/science/article/pii/S0169260724001834
Artificial intelligence in veterinary diagnostic imaging: Perspectives and limitations
Published in Research in Veterinary Science, 2024
Survey presenting the current trends in AI and veterinary imaging.
Recommended citation: Silvia Burti, et al., Artificial intelligence in veterinary diagnostic imaging: Perspectives and limitations, Research in Veterinary Science, 2024. https://www.sciencedirect.com/science/article/pii/S0034528824001838
HaN-Seg: The head and neck organ-at-risk CT and MR segmentation challenge
Published in Radiotherapy and Oncology, 2024
Description of the HaN-Seg challenge outcomes dedicated to multimodal segmentation of head and neck organs-at-risk
Recommended citation: Gašper Podobnik, et al., HaN-Seg: The head and neck organ-at-risk CT and MR segmentation challenge, Radiotherapy and Oncology, 2024. https://www.sciencedirect.com/science/article/pii/S0167814024006807
EsmTemp-Transfer Learning Approach for Predicting Protein Thermostability
Published in International Conference on Computational Science, 2024
Description of a deep learning-based method to predict protein thermostability using text-dedicated transformers
Recommended citation: A. Sułek, J. Jończyk, P. Orzechowski, AA. Hamed, M. Wodzinski, EsmTemp-Transfer Learning Approach for Predicting Protein Thermostabilitye, International Conference on Computational Science, 2024. https://link.springer.com/chapter/10.1007/978-3-031-63759-9_23
The ACROBAT 2022 challenge: automatic registration of breast cancer tissue
Published in Medical Image Analysis, 2024
Description of the ACROBAT 2022 challenge outcomes dedicated to automatic registration of breast cancer tissues.
Recommended citation: Philippe Weitz, et al., The ACROBAT 2022 challenge: automatic registration of breast cancer tissue, Medical Image Analysis, 2024. https://www.sciencedirect.com/science/article/pii/S1361841524001828
Automatic Cranial Defect Reconstruction with Self-Supervised Deep Deformable Masked Autoencoders
Published in IEEE EMBC 2024, 2024
This work presents deformable masked autoencoders in the context of automatic cranial defect reconstruction
Recommended citation: Marek Wodzinski, Daria Hemmerling, Mateusz Daniol, Automatic Cranial Defect Reconstruction with Self-Supervised Deep Deformable Masked Autoencoders, IEEE EMBC 2024, 2024 https://arxiv.org/abs/2404.13106
Eye-tracking in Mixed Reality for Diagnosis of Neurodegenerative Diseases
Published in IEEE EMBC 2024, 2024
Eye-tracking technique to extract features associated with neurodegenerative diseases.
Recommended citation: Mateusz Daniol et al. Eye-tracking in Mixed Reality for Diagnosis of Neurodegenerative Diseases, IEEE EMBC 2024, 2024. https://arxiv.org/abs/2404.1298
Improving quality control of whole slide images by explicit artifact augmentation
Published in Scientific Reports, 2024
Article presenting augmentation method to improve the generalizability of deep learning networks dedicated to quality control of WSIs.
Recommended citation: Artur Jurgas, Marek Wodzinski, Marina D’Amato, Jeroen van der Laak, Manfredo Atzori, Henning Müller, Improving quality control of whole slide images by explicit artifact augmentation, Scientific Reports, 2024. https://www.nature.com/articles/s41598-024-68667-2
Development of an artificial intelligence-based algorithm for predicting the severity of myxomatous mitral valve disease from thoracic radiographs by using two grading systems
Published in Research in Veterinary Science, 2024
Comparison of two grading systems to train and evaluate the severity of myxomatous mitral valve disease from thoracic radiographs.
Recommended citation: Carlotta Valente, Marek Wodzinski, Carlo Guglielmini, et al., Development of an artificial intelligence-based algorithm for predicting the severity of myxomatous mitral valve disease from thoracic radiographs by using two grading systems, Research in Veterinary Science, 2024. https://www.sciencedirect.com/science/article/pii/S0034528824002443
Multimodal representations of biomedical knowledge from limited training whole slide images and reports using deep learning
Published in Medical Image Analysis, 2024
Multimodal approach to classify WSIs using both visual and text data.
Recommended citation: Niccolò Marini, Stefano Marchesin, Marek Wodzinski, et al., Multimodal representations of biomedical knowledge from limited training whole slide images and reports using deep learning, Medical Image Analysis, 2024. https://www.sciencedirect.com/science/article/pii/S1361841524002287
Patch-Based Encoder-Decoder Architecture For Automatic Transmitted Light To Fluorescence Imaging Transition: Contribution To The Lightmycells Challenge
Published in IEEE ISBI, 2024
Description of the contribution to the LightMyCells challenge organized during IEEE ISBI 2024 - 3rd place.
Recommended citation: Marek Wodzinski; Henning Müller, Patch-Based Encoder-Decoder Architecture For Automatic Transmitted Light To Fluorescence Imaging Transition: Contribution To The Lightmycells Challenge, IEEE ISBI, 2024. https://ieeexplore.ieee.org/document/10635768
Improving deep learning-based automatic cranial defect reconstruction by heavy data augmentation: From image registration to latent diffusion models
Published in Computers in Biology and Medicine, 2024
Contribution presenting the influence of various augmentation strategies to automatic cranial defect reconstruction with deep learning methods.
Recommended citation: Marek Wodzinski, et al., Improving deep learning-based automatic cranial defect reconstruction by heavy data augmentation: From image registration to latent diffusion models, Computers in Biology and Medicine, Vol. 182, 2024. https://www.sciencedirect.com/science/article/pii/S0010482524012149
Automatic Registration of SHG and H&E Images with Feature-Based Initial Alignment and Intensity-Based Instance Optimization: Contribution to the COMULIS Challenge
Published in MICCAI 2024 - WBIR, 2024
Description of the method based on DeeperHistReg framework to the automatic registration of SHG and H&E slides.
Recommended citation: M. Wodzinski, H. Müller, Automatic Registration of SHG and H&E Images with Feature-based Initial Alignment and Intensity-based Instance Optimization: Contribution to the COMULIS Challenge, WBIR 2024, 2024. https://link.springer.com/chapter/10.1007/978-3-031-73480-9_27
Unsupervised Skull Segmentation via Contrastive MR-to-CT Modality Translation
Published in ACCV 2024, 2024
Description of an unsupervised skull segmentation method from MR volumes using deep modality transfer.
Recommended citation: K. Kwarciak, M. Daniol, D. Hemmerling, M. Wodzinski, Unsupervised Skull Segmentation via Contrastive MR-to-CT Modality Translation, ACCV 2024, 2024. https://arxiv.org/abs/2410.13427
Segmenting the Inferior Alveolar Canal in CBCTs Volumes: the ToothFairy Challenge
Published in IEEE Transactions on Medical Imaging, 2024
The ToothFairy challenge, organized within MICCAI 2023, provided a public dataset of 443 CBCT scans to benchmark and encourage deep learning research for the segmentation of the Inferior Alveolar Canal (IAC), resulting in the first comprehensive comparative evaluation of IAC segmentation methods.
Recommended citation: Federico Bolelli, et al., Segmenting the Inferior Alveolar Canal in CBCTs Volumes: the ToothFairy Challenge, IEEE Transactions on Medical Imaging, 2024. https://ieeexplore.ieee.org/abstract/document/10816445
Automatic detection and multi-component segmentation of brain metastases in longitudinal MRI
Published in Scientific Reports, 2024
Study on automated detection and segmentation of brain metastases in longitudinal MRI, focusing on enhancing lesion, edema, and necrosis components.
Recommended citation: Vincent Andrearczyk, et al., Automatic detection and multi-component segmentation of brain metastases in longitudinal MRI, Scientific Reports, Vol. 14, 2024. https://www.nature.com/articles/s41598-024-78865-7
MedShapeNet – a large-scale dataset of 3D medical shapes for computer vision
Published in Biomedical Engineering / Biomedizinische Technik, 2024
Introduction of MedShapeNet, a comprehensive dataset designed to bridge data-driven vision algorithms with medical applications.
Recommended citation: Jianning Li, et al., MedShapeNet – a large-scale dataset of 3D medical shapes for computer vision, Biomedical Engineering / Biomedizinische Technik, Vol. 69, 2024. https://www.degruyter.com/document/doi/10.1515/bmt-2024-0396/html
Artificial Intelligence-Empowered Radiology—Current Status and Critical Review
Published in Diagnostics, 2025
Review discussing the advancements in AI-based imaging applications, highlighting AI’s transformative potential for enhanced diagnostic support.
Recommended citation: Rafał Obuchowicz, et al., Artificial Intelligence-Empowered Radiology—Current Status and Critical Review, Diagnostics, Vol. 15, 2025. https://www.mdpi.com/2075-4418/15/3/282
Automatic Skull Reconstruction by Deep Learnable Symmetry Enforcement
Published in Computer Methods and Programs in Biomedicine, 2025
Description of a method for automatic skull reconstruction using dedicated symmetry loss that can be incorporated both during the training and inference phases. The method significantly improves the reconstruction outcomes for out-of-distribution cases.
Recommended citation: Marek Wodzinski, et al., Automatic Skull Reconstruction by Deep Learnable Symmetry Enforcement, Computer Methods and Programs in Biomedicine, 2025. https://www.sciencedirect.com/science/article/pii/S0169260725000872
Gait analysis in mixed reality for Parkinson’s disease assessment
Published in Biomedical Signal Processing and Control, 2025
Description of Parkinsons disease assessment using gait signals acquired by devices dedicated to mixed reality.
Recommended citation: Daria Hemmerling, et al., Gait analysis in mixed reality for Parkinson’s disease assessment, Biomedical Signal Processing and Control, 2025. https://www.sciencedirect.com/science/article/pii/S1746809425001703
Analysis of Voice, Speech, and Language Biomarkers of Parkinsons Disease Collected in a Mixed Reality Setting
Published in Sensors, 2025
Article presenting the possibility to diagnose Parkinsons disease using voice, speech and language biomarkers acquired in mixed reality.
Recommended citation: Milosz Dudek, et al., Analysis of Voice, Speech, and Language Biomarkers of Parkinson’s Disease Collected in a Mixed Reality Setting, Sensors, 2025. https://www.mdpi.com/1424-8220/25/8/2405
Automated determination of hip arthrosis on the Kellgren–Lawrence scale in pelvic digital radiographs scans using machine learning
Published in Computer Methods and Programs in Biomedicine, 2025
Description of a deep learning-based method dedicated to automatic determination of hip arthrosis from radiographs.
Recommended citation: Karolina Nurzynska, Marek Wodzinski, Adam Piorkowski, et al., Automated determination of hip arthrosis on the Kellgren–Lawrence scale in pelvic digital radiographs scans using machine learning, Computer Methods and Programs in Biomedicine, 2025. https://www.sciencedirect.com/science/article/pii/S0169260725001592
3-D Image-to-Image Fusion in Lightsheet Microscopy by Two-Step Adversarial Network: Contribution to the FuseMyCells Challenge
Published in IEEE ISBI 2025, 2025
Description of my contribution to the FuseMyCells organized during the IEEE ISBI 2025 conference in Houston, Texas - the contribution scored the 1st place in the competition.
Recommended citation: M. Wodzinski, H. Muller, 3-D Image-to-Image Fusion in Lightsheet Microscopy by Two-Step Adversarial Network: Contribution to the FuseMyCells Challenge, IEEE ISBI 2025, 2025. https://arxiv.org/abs/2503.16075
Automatic Multi-structure Segmentation in Cone Beam Computed Tomography Volumes Using Deep Encoder-Decoder Architectures
Published in MICCAI 2024 - ToothFairy, 2025
Description of the my contribution to the ToothFairy 2024 challenge related to automatic segmentation of dental structures.
Recommended citation: M. Wodzinski, H. Müller, Automatic Multi-structure Segmentation in Cone Beam Computed Tomography Volumes Using Deep Encoder-Decoder Architectures, MICCAI ToothFairy 2024, 2024. https://link.springer.com/chapter/10.1007/978-3-031-88977-6_7
Unsupervised skull segmentation in MR images utilizing modality translation and super-resolution
Published in Scientific Reports, 2025
Description of our contribution to automatic skull segmentation from MR volumes without using any ground-truth annotations
Recommended citation: Kamil Kwarciak, Mateusz Daniol, Daria Hemmerling, Marek Wodzinski, Unsupervised skull segmentation in MR images utilizing modality translation and super-resolution, Scientific Reports, 2025. https://www.nature.com/articles/s41598-025-05323-3
talks
MICCAI 2023 - Several Presentations
Published:
Several presentation were given during the MICCAI conference in Vancouver, Cananda MICCAI with a great success!
Smart Development Forum
Published:
I have recently presented the DeepImplant project during the Smart Development Forum organized in Uniejów, Poland FIR.
Presentation at IEEE ISBI 2024
Published:
I am happy to share a recent talk related to our contribution to the LightMyCells challenge organized by France BioImaging during IEEE ISBI 2024! Our method scored the 3rd place during the competition. The description of the challenge and the proposed method can be found at: Challenge Method
Two presentations during ECDP 2024
Published:
Two presentation discussing our recent papers were given during the European Congress on Digital Pathology (ECDP) in Vilnius, Lithuania ECDP. I presented the DeeperHistReg (RegWSI) framework - the state-of-the-art method dedicated to WSI registration, and our recent contirubtion to the WSI quality control by explicit artifact augmentation.
Presentation at IEEE EMBC 2024
Published:
A presentation of our recent paper presenting self-supervised deformable autoencoder was given during the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE EMBC 2024) in Orlando, Florida. The presentation is a part of the DeepImplant project.
Presentation at MICCAI 2024 - BraTS-Path
Published:
A talk presenting my contribution to the Brats-Path Challenge was given during the MICCAI 2024 in Marrakech, Morocco.
Presentation at ISBI 2025 - FuseMyCells
Published:
A talk presenting my contribution to the FuseMyCells Challenge was given during the IEEE ISBI 2025 in Houston, Texas.
Presentation at ITIB 2025 - DeepImplant
Published:
A talk presenting our contribution to the DeepImplant Project was given during the ITIB 2025 in Zabrze, Poland.
teaching
Introduction to Data Analysis
Undergraduate Course, AGH Univeristy of Kraków, 2022
Course introducing students to the basics of data analysis. Teaching between 2018 and 2020.
Deep Learning in Medical Image Analysis
Graduate Course, AGH University of Kraków, 2024
Course introducing to Deep Learning in Medical Image Analysis. Teaching from 2022.
Introduction to Numerical Methods
Undergraduate Course, AGH Univeristy of Kraków, 2024
Course introducing students to the basics of numerical methods in the context of data science and artificial intelligence. Teaching from 2020.
Medical Imaging Techniques
Undergraduate, AGH University of Kraków, 2024
Introductory course related to medical imaging techniques and image processing & analysis. Teaching from 2020.
Introduction to Python Programming
Undergraduate Course, AGH Univeristy of Kraków, 2024
Course introducing students to Python, especially in the context of Data Science and Artificial Intelligence. Teaching from 2024.