End-to-end model for automatic seizure detection using supervised contrastive learning

Recent advancements in multimodal machine learning have empowered the development of accurate and robust AI systems in the medical domain, especially within centralized database systems. Simultaneously, Federated Learning (FL) has progressed, providing a decentralized mechanism where data need not be consolidated, thereby enhancing the privacy and security of sensitive healthcare data. The integration of these two concepts supports the ongoing progress of multimodal learning in healthcare while ensuring the security and privacy of patient records within local data-holding agencies. This paper offers a concise overview of the significance of FL in healthcare and outlines the current state-of-the-art approaches to Multimodal Federated Learning (MMFL) within the healthcare domain. It comprehensively examines the existing challenges in the field, shedding light on the limitations of present models. Finally, the paper outlines potential directions for future advancements in the field, aiming to bridge the gap between cutting-edge AI technology and the imperative need for patient data privacy in healthcare applications.

pdf/multimodal-federated-learning-in-healthcare-a-review-36adpp3dxg.pdf

Multimodal Federated Learning in Healthcare: a review

CoSENT: Consistent Sentence Embedding via Similarity Ranking

In recent years, deep learning approaches have shown remarkable advancements in Multivariate Time Series Classification (MTSC) tasks. However, the existing approaches primarily focus on capturing the long-term correlations of time series or identifying local key sequence fragments, inevitably neglecting the synergistic properties between global and local components. Additionally, most representation learning methods for MTSC rely on self-supervised learning, which limits their ability to fully exploit label information. Hence, this paper proposes a novel approach termed Dual-Stream Encoder and Dual-Level Contrastive Learning Network (DSDCLNet), which integrates a Dual-Stream Encoder (DSE) and Dual-level Contrastive Learning (DCL). First, to extract multiscale local-global features from multivariate time series data, we employ a DSE architecture comprising an Attention-Gated Recurrent Unit (AGRU) and a Dual-layer Multiscale Convolutional Neural Network (DMSCNN). Specifically, DMSCNN consists of a series of multi-scale convolutional layers and a max pooling layer. Second, to maximize the utilization of label information, a new loss function is designed, which combines classification loss, instance-level contrastive loss, and temporal-level contrastive loss. Finally, experiments are conducted on the UEA datasets and the results demonstrate that DSDCLNet achieves the highest average accuracy of 0.77, outperforming traditional approaches, deep learning approaches, and self-supervised approaches on 30, 23, and 27 datasets, respectively. 

DSDCLNet: Dual-Stream Encoder and Dual-Level Contrastive Learning Network for Supervised Multivariate Time Series Classification

MADMM: microservice system anomaly detection via multi-modal data and multi-feature extraction

The pandemic of coronavirus disease 2019 (COVID-19) has led to a global public health crisis, which caused millions of deaths and billions of infections, greatly increasing the pressure on medical resources. With the continuous emergence of viral mutations, developing automated tools for COVID-19 diagnosis is highly desired to assist the clinical diagnosis and reduce the tedious workload of image interpretation. However, medical images in a single site are usually of a limited amount or weakly labeled, while integrating data scattered around different institutions to build effective models is not allowed due to data policy restrictions. In this article, we propose a novel privacy-preserving cross-site framework for COVID-19 diagnosis with multimodal data, seeking to effectively leverage heterogeneous data from multiple parties while preserving patients' privacy. Specifically, a Siamese branched network is introduced as the backbone to capture inherent relationships across heterogeneous samples. The redesigned network is capable of handling semisupervised inputs in multimodalities and conducting task-specific training, in order to improve the model performance of various scenarios. The framework achieves significant improvement compared with state-of-the-art methods, as we demonstrate through extensive simulations on real-world datasets.

A Collaborative Multimodal Learning-Based Framework for COVID-19 Diagnosis.

Text classification is a longstanding research topic in natural language processing (NLP). Deep learning has emerged as an effective paradigm for solving text classification problems. However, the performance of a deep model is heavily reliant on large-scale human-annotated data. In this paper, we propose a Semi-Supervised Contrastive Learning (SSCL) framework for text classification, which can significantly improve the performance of deep models in the case of limited labeled data. The proposed framework consists of two components: a pseudo label generation strategy and a contrastive learning scheme for text classification. We first devise a prompt-based strategy for training Bidirectional Encoder Representation from Transformers (BERT), with a small amount of human-labeled data, to obtain a task-correlation model capable of generating pseudo labels for unlabeled text. Then, for the text classification task, we use a two-step contrastive learning scheme: pre-training a deep model with pseudo labels as supervision to capture inter-class patterns while mitigating the negative impact of pseudo label noise, and then fine-tuning the pre-trained model with human-labeled data using a supervised contrastive learning approach. Benefiting from the generated pseudo labels and anti-noise contrastive pre-training, we only use a small amount of labeled data during the training process for the downstream text classification tasks. Experimental results on the twitter sentiment classification dataset and the aspect classification dataset show that our method significantly outperforms baseline methods in a few-shot setting.

Few-Shot Text Classification via Semi-Supervised Contrastive Learning

Representation learning from noisy user-tagged data for sentiment classification

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A Collaborative Multimodal Learning-Based Framework for COVID-19 Diagnosis.

Few-Shot Text Classification via Semi-Supervised Contrastive Learning

Representation learning from noisy user-tagged data for sentiment classification