Huaqi Qiu | AI in Medicine

Master-Seminar: Implicit Neural Representation and Neural Fields (IN2107)

Sun, 16 Feb 2025 00:00:00 +0000

In this summer semester (2025S), we are offering a master’s seminar course on the topic of “Implicit Neural Representation and Neural Fields”.

This seminar course will explore implicit neural representations (INR) and Neural Fields, an area of deep learning that uses neural networks to model complex functional mappings from coordinates to various field quantities such as radiance, image intensity, or density. These methods have exciting applications in scene representation, image enhancement, novel view and temporal frame synthesis, shape modelling, physics simulations, data compression, and many more.

Implicit neural representations offer powerful alternatives to traditional data structure and representation, enabling compact and flexible modeling of the underlying entities with resolution limit. Neural Fields is a broader class of techniques that extends these capabilities to represent complex structures such as surfaces, volumes, and dynamic phenomena, making them highly relevant for modern data-driven methods. At their core, research on these methods aim to look beyond the structures in which data representing an entity is commonly sampled and presented (e.g. images, meshes, point clouds) by building modeling tasks around the underlying geometry.

In this seminar, we will overview different aspects of INRs and Neural Fields through the discussion of a serious of papers from research literature. We look at the theoretical fundamental of implicit representations and neural fields, by looking at seminal works in compression and interpolation, as well as their connections to general topics in signal processing and geometric deep learning. We will also explore recent advancements, such as the integration of prior knowledge through physics-informed neural networks, using data cohorts to condition the modeling process, and multimodal data fusion. In-depth discussions of the research papers will allow students to understand and critically analyze these methods.

For more information, please see the TUMOnline page and the information slides below.

Information Slides

Please sign-up at: https://matching.in.tum.de/ or write an e-mail to: harvey.qiu@tum.de

Master-Seminar: Multi-modal AI for Medicine (IN2107)

Mon, 15 Jul 2024 00:00:00 +0000

This year’s seminar will look at aspects of multi-modal machine learning in medicine and healthcare, focusing on:

Vision language models (VLMs) for medical and healthcare applications
Generic multi-modal AI models utilising imaging data, clinical reports, lab test results, electronic health records, and genomics
Foundation models for multi-modal medicine

Objectives:

At the end of the module students should have:

a thorough understanding of current research in multi-modal AI in medicine, in particular about foundation models and large vision-language models and their impact in medicine
After course completion students should be able to apply learned concepts, critically evaluate research works in the area, and be able to conceptualise strategies to tackle the issues discussed

Methods:

Each student will choose one paper from a provided list of papers, read it, and give a 15-minute presentation about the paper during the seminar sessions
All students are expected and highly encouraged to participate in discussions during the seminar sessions
Each student will then write a 2-page report after presenting and discussing the paper

Prerequisites:

Students are expected to be familiar with:

Mathematics basics (graduate level):
- probability theory
- linear algebra
- calculus
Machine / deep learning basics, e.g. having completed:
- Machine Learning (IN2064)
- Introduction to Deep Learning

Preference might be given to students with:

Knowledge in deep learning models in medicine, especially vision and/or language models
Completion of related courses from our chair, e.g.:
- AI in Medicine I
- AI in Medicine II
Work experience in AI / Data Science for Medicine & Healthcare

An online information meeting will take place on 15 July, 16:00 via Zoom (https://tum-conf.zoom-x.de/j/64109399034?pwd=zbcYd1t9e91fy3DqfHyG7NULPyMcsl.1)
You can sign up for the course in the matching system (https://matching.in.tum.de/m/mwvrjkg/q/fd56hbnn2x)
Please fill in the following form in addition to voting in the matching system. The information you provided will help us to evaluate our votes: https://forms.gle/xTbgwcFf1ZeaDeXT7

Information Slides - 2024/25 Winter Semester