Deep learning for medical image interpretation
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Deep learning methods for medical image computing., url to cite or link to: http://hdl.handle.net/1802/35662.
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Multimodal Representation Learning for Medical Image Analysis
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Deep Learning Techniques for Multi-Dimensional Medical Image Analysis
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Research output : Thesis › Phd Thesis 1 (Research TU/e / Graduation TU/e)
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- 20230927_Ghazvinian_Zanjani_hf Final published version, 9.2 MB
T1 - Deep Learning Techniques for Multi-Dimensional Medical Image Analysis
AU - Ghazvinian Zanjani, Farhad
N1 - Proefschrift.
PY - 2023/9/27
Y1 - 2023/9/27
M3 - Phd Thesis 1 (Research TU/e / Graduation TU/e)
SN - 978-90-386-5817-9
PB - Eindhoven University of Technology
CY - Eindhoven
We are the Machine Learning in Medical Image Analysis Group at the Universities of Tübingen, Germany and Lucerne, Switzerland
Recent news, machine learning in medical image analysis, bridging the gap between ai and clinical practice, cluster of excellence: machine learning - new perspectives for science - university of tübingen, germany, faculty of health sciences and medicine - university of lucerne, switzerland, our research.
In the field of medical image analysis, the ultimate goal is to improve patient outcomes. Machine learning can help to achieve this goal by
- Accelerating and simplifying the analysis of medical images through (partial) automation of diagnosis, outcome prediction, image quantification, and image reconstruction.
- Developing technology which enables completely novel clinical workflows which are not possible without AI support.
- Extraction of new clinical knowledge from large image databases, which can inform future clinical decisions, treatments and drug trials.
Even though tremendous progress has been made for all of those points in research settings, surprisingly little of this technology has made it into medical practice. One reason for this is that the medical domain is an extremely high-stakes application field with extraordinary demands on robustness of algorithms. Another is that algorithmic outputs are not suitable for clinical decision-making if neither the patient nor the doctor can understand the reasoning behind the prediction, and clinicians are loath to use the thus-far predominately black-box technology. Both of the above points also have important implications for the certification of AI technology.
Therefore, in order to start harnessing the massive potential of machine learning for healthcare, and to actually use it to improve real patient outcomes, the Machine Learning in Medical Image Analysis group aims to do research that helps to bridge this gap between machine learning and clinical practice. We perform this research along four broad directions:
- Robustness, Safety and Uncertainty
- Interpretable Machine Learning
- Human-in-the-Loop Machine Learning Systems
- Generative Modelling on Big Medical Datasets
These topics are described in more detail in the research areas section below.
We are part of the Cluster of Excellence: Machine Learning - New Perspectives for Science and the University of Tübingen .
The group is headed by Dr. Christian Baumgartner .
Find a list of the MLMIA alumni here .
Group Leader
Christian baumgartner, independent research group leader, phd students, alexander frotscher, phd student.
Anomaly detection using generative models, (co-supervision with Dr. Thomas Wolfers)
Jaivardhan Kapoor
Probabilistic inference in spatio-temporal models
Nikolas Morshuis
Robust MRI analysis and reconstruction using physics-informed networks
Paul Fischer
Uncertainty quantification in medical prediction systems
Stefano Woerner
Few-shot and meta-learning for learning from few data
Interpretable Machine Learning, Incorporation of Prior Domain Knowledge into Deep Neural Networks
Master Students
Anna wundram, master thesis student.
Uncertainty estimation in glaucoma diagnosis
Other Researchers
David jakobs, medical doctoral student.
Optimal clinical human-AI collaboration, (co-supervision with Prof. Dr. med. Sergios Gatidis)
Research Areas
Recent talks, open positions.
- +49 7071 29-70847
- Maria-von-Linden-Straße 6, AI Research Building, R. 40-5/A4, Tübingen, Baden-Württemberg 72076
IMAGES
VIDEO
COMMENTS
The emergence of medical image analysis using deep learning techniques has ... given me infinite grace, knowledge, and opportunity to complete my PhD study and this thesis. I would like to convey my heartfelt thanks to my supervisory team, including my first supervisor, Dr. Mohammed Abdelsamea, and my second supervisor, Prof. ...
Abstract. There have been rapid advances at the intersection of deep learning and medicine over the last few years, especially for the interpretation of medical images. In this thesis, I describe three key directions that present challenges and opportunities for the development of deep learning technologies for medical image interpretation.
This thesis develops deep learning models and techniques for medical image analysis, reconstruction and synthesis. In medical image analysis, we concentrate on understanding the content of the medical images and giving guidance to medical practitioners. In particular, we investigate deep learning ways to address classification, detection ...
Wang, Chuanbo, "Medical Image Segmentation with Deep Learning" (2020). Theses and Dissertations. 2434. https://dc.uwm.edu/etd/2434. This Thesis is brought to you for free and open access by UWM Digital Commons. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of UWM Digital Commons.
Dissertation presented at Uppsala University to be publicly examined in Room IX, Universitetshuset, Biskopsgatan 3, Uppsala, Wednesday, 12 May 2021 at 13:00 for the degree ... heterogeneity", Computational Pathology and Ophthalmic Medical Image Analysis. COMPAY 2018, published in Lecture Notes in Computer Science book series (LNCS, volume ...
Abstract. My thesis develops machine learning methods that exploit multimodal clinical data to improve medical image analysis. Medical images capture rich information of a patient's physiological and disease status, central in clinical practice and research. Computational models, such as artificial neural networks, enable automatic and ...
Explanations justify the development and adoption of algorithmic solutions for prediction problems in medical image analysis. This thesis introduces two guiding principles for creating and exploiting explanations of deep networks and medical image data. The first guiding principle is to use explanations to expose inefficiencies in the design of models and image datasets. The second principle ...
7.2.2 Neural Architecture Search. For existing deep learning architectures of medical image analysis, the model design heavily relies on the experience of arti cial intelligence (AI) researchers and needs higher requirements for radiologists who only have medical background and lack of computer-aided system knowledge.
This Master Thesis provides a summary overview on the use of current deep learning-based object detection methods for the analysis of medical images, in particular from microscopic tissue sections, and aims at making the results reproducible. This Master Thesis provides a summary overview on the use of current deep learning-based object detection methods for the analysis of medical images, in ...
Deep Learning Techniques for Multi-Dimensional Medical Image Analysis. / Ghazvinian Zanjani, Farhad. Eindhoven: Eindhoven University of Technology, 2023. 183 p. ... M3 - Phd Thesis 1 (Research TU/e / Graduation TU/e) SN - 978-90-386-5817-9. PB - Eindhoven University of Technology. CY - Eindhoven.
The purpose of this Special Issue (SI) "Deep Learning in Medical Image Analysis" is to present and highlight novel algorithms, architectures, techniques, and applications of DL for medical image analysis. This SI called for papers in April 2020. It received more than 60 submissions from over 30 different countries.
PHD. Synopsis. Medical image analysis is a research field where advanced image analysis techniques are developed to solve or analyse medical problems, e.g., designing models to predict, diagnose or monitor diseases. Computer-assisted automatic processing and analysis of medical images is in high demand due to its better precision, repeatability ...
A THESIS SUBMITTED TO . THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES . OF . MIDDLE EAST TECHNICAL UNIVERSITY . BY . ALPER ÇEVİK. ... Medical image analysis is one of the most critical studies in field of medicine, since results gained by the analysis guide radiologists for diagnosis,
Further analysis also indicates that our proposed network components indeed contribute to the performance gain. Experiments on an extra dataset also validate the generalization ability of our proposed methods. Generative adversarial networks (GANs) are widely used in medical image analysis tasks, such as medical image segmentation and synthesis.
I'm interested. Contact us. Institute of Machine Learning in Biomedical Imaging, Helmholtz Center Munich. Ingolstädter Landstr. 1, 85764 Neuherberg. [email protected]. Tel. +49 89 3187-49207. Computational Imaging and AI in Medicine, Technical University of Munich. Lichtenbergstr. 2a, 85748 Garching. [email protected].
On Deep Learning for Medical Image Analysis. Neural networks, a subclass of methods in the broader field of machine learning, are highly effective in enabling computer systems to analyze data, facilitating the work of clinicians. Neural networks have been used since the 1980s, with convolutional neural networks (CNNs) applied to images ...
Research Studentship in Human-AI Collaboration in Medical Imaging. 3.5-year D.Phil. studentship . Project. Human-AI Collaboration in Medical Imaging. Read more. Supervisor: Prof A Noble. 3 December 2024 PhD Research Project Funded PhD Project (UK Students Only) More Details.
Build your expertise in AI-powered medical imaging and radical healthcare innovations, on a multidisciplinary MRes and MPhil/PhD. ... A dissertation of up to 100,000 words for a PhD, or up to 60,000 words for an MPhil, is completed as a part of this programme. ... Image Computing: image analysis; computational modelling. Integrated Systems: ...
Course number ML-4506. Last updated on Jun 3, 2022. Contact. Send. +49 7071 29-70847. Maria-von-Linden-Straße 6, AI Research Building, R. 40-5/A4, Tübingen, Baden-Württemberg 72076. Published with Wowchemy — the free, open source website builder that empowers creators. The Machine Learning in Medical Image Analysis (MLMIA) group, Cluster ...
Analysis of medical images is essential in modern medicine. With the increasing amount of patient data, new challenges and opportunities arise for different phases of the clinical routine, such as diagnosis, treatment and monitoring. The InnerEye research project focuses on the automatic analysis of patients' medical images. It uses state of the art machine learning […]