Flagship: Computing for Health
Foundation Models for Biosignal Analysis [Luca Benini]
Biosignal analysis is a field of growing interest, also thanks to the increased availability of wearable devices for continuous data acquisition and monitoring. Among biosignals, electroencephalography (EEG) is of particular interest as it offers essential insights into the operation of the brain, aiding in the diagnosis and treatment of various diseases, and playing a key role in brain-machine interfaces. However, analyzing EEG signals presents considerable challenges due to their complexity and the need for precise differentiation between the background and the activities of the brain of interest. This project aims to advance the analysis of EEG signals employing foundation models and innovative deep learning techniques. Recent developments applied Transformer architectures to EEG data for superior representation learning. These approaches highlighted the potential of AI in improving EEG signal analysis, with promising applications in various classification tasks such as sleep stage and seizure classification, emotion recognition, and motor imagery classification. However, the exploration of foundation models for biosignal analysis is still at its infancy. In this project, our aim is to develop foundation models for EEG signal analysis by investigating data augmentation strategies, tokenization methods, self-supervised pre-training strategies, and model architecture design (Transformers or Mamba). Leveraging lessons learned from these developments on EEG signals, the project will also explore multimodality and foundation models for alternative physiological signals (such as PPG, EMG, or ultrasound).
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chevron_right Yawei LiFall Injury Classification [Torsten Hoefler]
Our objective is to decrease the rate of hospital admissions due to fall-related injuries while also minimizing the complications that arise when such injuries do occur.
The injury risk to the hip when falling can be assessed by using medical imaging techniques to build a three-dimensional model of a person’s hip, which is then used as input for finite element simulations of different fall scenarios. Such simulations are computationally expensive, and its inputs involve confidential patient data, and thus cannot be performed in the cloud.
The key to this project is to reduce the computation cost of fall-related hip injury prediction using AI methods. The most effective way to do this is not known, we will investigate multiple approaches, such as training a GNN on voxel data (which completely replaces the existing simulation pipeline) and using automatic differentiation to accelerate the existing simulation.
We will train our models using federated learning, enabling decentralized training while preserving data privacy. This approach allows collaboration without sharing sensitive data, improving model accuracy with a diverse dataset. We aim to train a large model on the server and then scale it down for use on client devices like those in hospitals, making it efficient for hardware with limited computational resources.
To summarize, our model will enable healthcare providers and individuals to take proactive steps to reduce fall-related injury risks, improving patient outcomes and lowering healthcare costs. With a privacy-focused, data-driven approach, we aim to advance fall prevention and intervention strategies.
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chevron_right Timo SchneiderMetagenomic Analysis on Near-Data-Processing Platforms [Onur Mutlu]
Metagenomics applications monitor the diversity of organisms in different environments and are critical in clinical practice and public health. State-of-the-art analyses rely on high throughput sequencing for microorganism identification but suffer from a huge memory footprint, frequent data movement, and high dependency on internet access. This causes intense congestion in the network, high power consumption, and raises privacy concerns. To this end, it is paramount to perform high-speed genome detection locally for screening and diagnostic purposes. General genomic accelerator designs are suboptimal for metagenomics acceleration as they are not tailored to the specific pipeline steps, they neglect end-to-end acceleration and do not consider the significantly larger amounts of data. The goal of this project is to accelerate metagenomics for small-edge devices leveraging the near-data-processing (NDP) paradigm, as it can effectively address the huge data size, massive data movement, and parallel computation requirements of metagenomics. We aim to perform a holistic study of metagenomic algorithms and exploit processing-near-memory or in-storage subsystem (PNM) and processing-using-memory (PUM) to accelerate metagenomics steps. The result will be an end-to-end NDP system that combines both PNM and PUM proposed accelerators in a co-designed approach that seamlessly integrates within a metagenomics pipeline.