Learning Against Modality Absence: State-of-the-Art Methods and Solution for Biomedical Applications

Seminar by Jingwei Zhang

Start: 3/05/2024, 11:00
Location: B00.35

Abstract: Multimodal deep learning systems, utilizing multiple modalities, have demonstrated superior performance over unimodal systems. However, prevalent assumptions in multimodal machine learning, such as all the modalities are present, well aligned, and noiseless during both training process and deployment, usually do not hold in real-world scenarios. To address these challenges, various methodologies have been proposed, including modality imputation, generation, zero-shot learning, and Cross-Modal Transfer Learning. This seminar starts with a general overview of these methodologies, followed by a discussion of their respective advantages, disadvantages, and suitability within biomedical contexts. Moreover, the seminar will demonstrate a novel solution tailored for biomedical signals. By showcasing its advantages in biomedical settings, this seminar aims to underscore the potential of robust multimodal machine learning techniques for advancing biomedical applications.

Organized by: Jingwei Zhang