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Found 19 papers, 3 papers with code
AIC-UNet: Anatomy-informed Cascaded UNet for Robust Multi-Organ Segmentation
Imposing key anatomical features, such as the number of organs, their shapes, sizes, and relative positions, is crucial for building a robust multi-organ segmentation model.
Learning the Unlearned: Mitigating Feature Suppression in Contrastive Learning
Self-Supervised Contrastive Learning has proven effective in deriving high-quality representations from unlabeled data.
The NUS-HLT System for ICASSP2024 ICMC-ASR Grand Challenge
This paper summarizes our team's efforts in both tracks of the ICMC-ASR Challenge for in-car multi-channel automatic speech recognition.
Selective HuBERT: Self-Supervised Pre-Training for Target Speaker in Clean and Mixture Speech
Self-supervised pre-trained speech models were shown effective for various downstream speech processing tasks.
A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics
This shift encompasses a move from discriminative AI approaches to generative AI approaches, as well as a shift from model-centered methodologies to datacentered methodologies.
A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods
Generally, this review aims to allow researchers from both clinical and technical backgrounds to gain a quick and yet in-depth understanding of the research in uncertainty quantification for medical image analysis machine learning models.
P-Transformer: A Prompt-based Multimodal Transformer Architecture For Medical Tabular Data
While deep learning approaches, particularly transformer-based models, have shown remarkable performance in tabular data prediction, there are still problems remained for existing work to be effectively adapted into medical domain, such as under-utilization of unstructured free-texts, limited exploration of textual information in structured data, and data corruption.
Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining Approach
In this paper, we study two types of bad positive pairs that can impair the quality of time series representation learned through contrastive learning: the noisy positive pair and the faulty positive pair.
FCSN: Global Context Aware Segmentation by Learning the Fourier Coefficients of Objects in Medical Images
In this work, we propose a Fourier Coefficient Segmentation Network~(FCSN) -- a novel DNN-based model that segments an object by learning the complex Fourier coefficients of the object's masks.
UFRC: A Unified Framework for Reliable COVID-19 Detection on Crowdsourced Cough Audio
We suggested a unified system with core components of data augmentation, ImageNet-pretrained ResNet-50, cost-sensitive loss, deep ensemble learning, and uncertainty estimation to quickly and consistently detect COVID-19 using acoustic evidence.
Intra-Inter Subject Self-supervised Learning for Multivariate Cardiac Signals
In inter subject self-supervision, we design a set of data augmentations according to the clinical characteristics of cardiac signals and perform contrastive learning among subjects to learn distinctive representations for various types of patients.
Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies
To some extent, current deep learning solutions can address these challenges.
DiCOVA-Net: Diagnosing COVID-19 using Acoustics based on Deep Residual Network for the DiCOVA Challenge 2021
In this paper, we propose a deep residual network-based method, namely the DiCOVA-Net, to identify COVID-19 infected patients based on the acoustic recording of their coughs.
Multi-task Graph Convolutional Neural Network for Calcification Morphology and Distribution Analysis in Mammograms
In this study, we proposed a multi-task deep graph convolutional network (GCN) method for the automatic characterization of morphology and distribution of microcalcifications in mammograms.
Identification of 27 abnormalities from multi-lead ECG signals: An ensembled Se-ResNet framework with Sign Loss function
Cardiovascular disease is a major threat to health and one of the primary causes of death globally.
Zoom in to where it matters: a hierarchical graph based model for mammogram analysis
In clinical practice, human radiologists actually review medical images with high resolution monitors and zoom into region of interests (ROIs) for a close-up examination.
Deep Reinforcement Learning for Clinical Decision Support: A Brief Survey
Owe to the recent advancements in Artificial Intelligence especially deep learning, many data-driven decision support systems have been implemented to facilitate medical doctors in delivering personalized care.
A Self-Correcting Deep Learning Approach to Predict Acute Conditions in Critical Care
Compared with the existing models, our method has a number of distinct features: we utilized the accumulative data of patients in ICU; we developed a self-correcting mechanism that feeds errors from the previous predictions back into the network; we also proposed a regularization method that takes into account not only the model's prediction error on the label but also its estimation errors on the input data.
MIMIC-III, a freely accessible critical care database
MIMIC-III (‘Medical Information Mart for Intensive Care’) is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital.
