Generative AI in Medicine
The Generative AI in Medicine (GenAI) group is at the forefront of deep learning research, tackling a wide range of topics involving data types such as image, text, and speech. Our work spans large language models, diffusion models, and GANs to ultimately develop foundation multimodal models. With a particular focus on medicine and healthcare, our multidisciplinary team of engineers and healthcare professionals collaborates to advance AI-driven solutions for diagnosis and prognosis.
Research topics
Large language models (LLMs):
- Radiology report generation
- Clinical note summarization
- Clinical decision-making support
- Question answering
- LLM-assisted diagnosis
- Automated machine learning and code generation
- Fine-tuning and retrieval augmented generation (RAG) for domain-specific tasks
Diffusion models and GANs:
- Image synthesization
- Speech and audio signal generation
- 2D and 3D medical image generation such as radiographs, CTs, and MRIs.
Foundation multimodal models:
- Vision-language models
- Speech-language models
- Speech-image models
Publications
Large language models streamline automated machine learning for clinical studies
In: Nature Communications 15 (2024), p. 1603 (non-FAU publication)
ISSN: 2041-1723
DOI: 10.1038/s41467-024-45879-8
BibTeX: Download
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Using machine learning to reduce the need for contrast agents in breast MRI through synthetic images
In: Radiology 307 (2023), Article No.: e222211 (non-FAU publication)
ISSN: 0033-8419
DOI: 10.1148/radiol.222211
BibTeX: Download
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Benchmarking ChatGPT-4 on a radiation oncology in-training exam and Red Journal Gray Zone cases: potentials and challenges for AI-assisted medical education and decision making in radiation oncology
In: Frontiers in Oncology 13 (2023), p. 1-13
ISSN: 2234-943X
DOI: 10.3389/fonc.2023.1265024
BibTeX: Download
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Steps to pursue your MA/BA thesis at the group
Before starting:
a) If you are an FAU student and would like to pursue your thesis from a company/external institution or university at the Pattern Recognition Lab:
- Follow the steps 1-5
b) If you are an FAU student and would like to pursue your thesis directly at the Pattern Recognition Lab:
- Directly get in touch with your supervisor instead of steps 1-5
Steps:
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Contact the supervisor from the PRLab and have a joint meeting with them and your company supervisor (if necessary another follow-up meeting) for discussion and organization, such as:
- Starting and finishing dates
- General tasks and expectations
- Communication mediums and frequency
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Prepare the thesis description (1-2 pages) PDF document after discussing it with your company supervisor
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A sample PDF, as well as the LaTeX template, are available from your PRLab supervisor.
- Some examples of online versions: 1, 2
Your description should contain the following parts:
- Motivation and background
- Materials and methods (only the public ones to be published in the thesis)
- Research goals of the thesis (only the public ones to be published in the thesis)
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Read about the general information and requirements for theses at our lab:
- https://lme.tf.fau.de/teaching/thesis/thesis-guidelines-english-version/
- Strictly follow the provided template and guidelines
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Finalize the binding thesis description, including:
- Agreement of the company supervisor
- Agreement of the PRLab supervisor
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Register your thesis officially at the University (your PRLab supervisor should help you with this). You need to have the following requirements:
- [Usually] Passed at least 70 ECTS for master’s students. Please check this carefully with your degree regulations before taking any steps
- Check with your degree regulations to see if you need to have further thesis reviewers/advisors from other departments/faculties (especially for students pursuing the M.Sc. in Data Science)
- Thesis description
- Enrollment document at FAU
- Registration happens with the secretary of the Pattern Recognition Lab
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Once your registration is complete, you should be able to see it on your FAU account.
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The time period for submitting your final thesis:
- For full-time students:
- Minimum 3 months
- Maximum 6 months (strictly binding)
- For part-time students:
- Minimum 6 months
- Maximum 12 months (strictly binding)
- For full-time students:
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The time period is binding and starts with your official registration
Once you started:
- Participation in the respective colloquium
- Choose the correct colloquium with the help of your PRLab supervisor and start attending. Every colloquium takes place on a weekly or biweekly basis. Please refer to the course list on the chair’s website for the individual day and time of the respective colloquium.
- Examples of suitable colloquia depending on your topic:
- If you are working with text data and LLMs: Human Speech and Language (SAGI) Colloquium
- If you are working with speech, audio, and time series: Time Series Intelligence (TSI) Colloquium
- If you are working with non-medical images: Computer Vision (CV) Colloquium
- If you are working with medical images: Medical Image Analysis Colloquium
- All your presentations also happen in the colloquium (make an appointment with your PRLab supervisor before the presentation).
- MT/BT intro talk:
- A short presentation of the topic at the beginning of the work
- A final presentation (defense) shortly before the end of the thesis:
- About 6 weeks before the thesis finishes, the final presentation takes place. This is a 30-minute talk that should include all the results the candidate has achieved so far. However, it is clear that these results are not necessarily “final”; our experience has shown that while discussing the work with a little more concrete data, different people often come up with interesting ideas. Thus, we find it reasonable to have such a discussion before the actual deadline for handing in the thesis. The templates for the slides are the same as for the short presentation.
Finishing your thesis:
- Finalize the thesis with the help of all your supervisors
- Once all the supervisors agree, you can submit your thesis:
- Source codes, networks, latex code, PDF format, etc. should be archived with the IT administration of the Lab and the PRLab supervisor
- Print the mandatory deposit copies of the PDF of the final thesis should be submitted to the secretary of the PRLab.
- Your PRLab supervisor(s) reviews your final submitted thesis, evaluates your performance during the thesis period, and considers your presentations before recommending a final grade to Prof. Maier.
- Your grade will then be sent to the secretary of the PRLab, who will forward it to the examination office.
- You will be notified of your final grade similar to other courses from the examination office.
- If you have already passed other requirements for your degree, the date of your MA/BA degree will be your MT/BT defense date.
Colloquia
Computer vision colloquium:
Mohammad Hasan Khan “Wind Power Forecasting through Probabilistic Machine Learning Models” (MT Intro)
Speech colloquium: