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Found 36 papers, 16 papers with code
RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Model
Text-to-image generation (TTI) refers to the usage of models that could process text input and generate high fidelity images based on text descriptions.
Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction:A Multi-Dataset Study
However, the label scarcity problem, the co-occurrence of multiple CVDs and the poor performance on unseen datasets greatly hinder the widespread application of deep learning-based models.
A Brief Review of Hypernetworks in Deep Learning
They offer a new way to design and train neural networks, and they have the potential to improve the performance of deep learning models on a variety of tasks.
Dynamic Inter-treatment Information Sharing for Heterogeneous Treatment Effects Estimation
Existing heterogeneous treatment effects learners, also known as conditional average treatment effects (CATE) learners, lack a general mechanism for end-to-end inter-treatment information sharing, and data have to be split among potential outcome functions to train CATE learners which can lead to biased estimates with limited observational datasets.
Data Encoding For Healthcare Data Democratisation and Information Leakage Prevention
Experimental evaluation highlights that models trained on encoded time-series data effectively uphold the information bottleneck principle and hence, exhibit lesser information leakage from trained models.
ZeroNLG: Aligning and Autoencoding Domains for Zero-Shot Multimodal and Multilingual Natural Language Generation
We present the results of extensive experiments on twelve NLG tasks, showing that, without using any labeled downstream pairs for training, ZeroNLG generates high-quality and believable outputs and significantly outperforms existing zero-shot methods.
Synthesizing Mixed-type Electronic Health Records using Diffusion Models
Electronic Health Records (EHRs) contain sensitive patient information, which presents privacy concerns when sharing such data.
Lightweight Transformers for Clinical Natural Language Processing
To our knowledge, this is the first comprehensive study specifically focused on creating efficient and compact transformers for clinical NLP tasks.
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective
For medical image segmentation, contrastive learning is the dominant practice to improve the quality of visual representations by contrasting semantically similar and dissimilar pairs of samples.
Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations
Most video-and-language representation learning approaches employ contrastive learning, e. g., CLIP, to project the video and text features into a common latent space according to the semantic similarities of text-video pairs.
Ranked #2 on
Video Retrieval
on LSMDC
(text-to-video Mean Rank metric)
Generating Accurate and Faithful Discharge Instructions: Task, Dataset, and Model
We build a benchmark clinical dataset and propose the Re3Writer, which imitates the working patterns of physicians to first retrieve related working experience from historical PIs written by physicians, then reason related medical knowledge.
Adversarial De-confounding in Individualised Treatment Effects Estimation
Observational studies have recently received significant attention from the machine learning community due to the increasingly available non-experimental observational data and the limitations of the experimental studies, such as considerable cost, impracticality, small and less representative sample sizes, etc.
MiniALBERT: Model Distillation via Parameter-Efficient Recursive Transformers
Different strategies have been proposed in the literature to alleviate these problems, with the aim to create effective compact models that nearly match the performance of their bloated counterparts with negligible performance losses.
Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels
Blindly leveraging all pixels in training hence can lead to the data imbalance issues, and cause deteriorated performance; (2) consistency: it remains unclear whether a segmentation model has learned meaningful and yet consistent anatomical features due to the intra-class variations between different anatomical features; and (3) diversity: the intra-slice correlations within the entire dataset have received significantly less attention.
On the Effectiveness of Compact Biomedical Transformers
Language models pre-trained on biomedical corpora, such as BioBERT, have recently shown promising results on downstream biomedical tasks.
Ranked #2 on
Named Entity Recognition (NER)
on BC2GM
COPER: Continuous Patient State Perceiver
COPER uses Perceiver model and the concept of neural ordinary differential equations (ODEs) to learn the continuous time dynamics of patient state, i. e., continuity of input space and continuity of output space.
Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression Modeling
A particular challenge for disease progression modeling is the heterogeneity of a disease and its manifestations in the patients.
ProSelfLC: Progressive Self Label Correction Towards A Low-Temperature Entropy State
For the issue (2), the effectiveness of ProSelfLC defends entropy minimisation.
Multimodal Learning with Transformers: A Survey
Transformer is a promising neural network learner, and has achieved great success in various machine learning tasks.
Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data
Advances in deep learning for human activity recognition have been relatively limited due to the lack of large labelled datasets.
Deep Reinforcement Learning for Multi-class Imbalanced Training
With the rapid growth of memory and computing power, datasets are becoming increasingly complex and imbalanced.
How to Understand Masked Autoencoders
"Masked Autoencoders (MAE) Are Scalable Vision Learners" revolutionizes the self-supervised learning method in that it not only achieves the state-of-the-art for image pre-training, but is also a milestone that bridges the gap between visual and linguistic masked autoencoding (BERT-style) pre-trainings.
Towards Scheduling Federated Deep Learning using Meta-Gradients for Inter-Hospital Learning
In this work, we aim to utilise patient data extracted from multiple hospital data centres to train a machine learning model without sacrificing patient privacy.
Not All Knowledge Is Created Equal: Mutual Distillation of Confident Knowledge
Additionally, CMD covers two special cases: zero-knowledge and all knowledge, leading to a unified MKD framework.
SoCal: Selective Oracle Questioning for Consistency-based Active Learning of Physiological Signals
The ubiquity and rate of collection of physiological signals produce large, unlabelled datasets.
PCPs: Patient Cardiac Prototypes
Many clinical deep learning algorithms are population-based and difficult to interpret.
CROCS: Clustering and Retrieval of Cardiac Signals Based on Patient Disease Class, Sex, and Age
The process of manually searching for relevant instances in, and extracting information from, clinical databases underpin a multitude of clinical tasks.
DROPS: Deep Retrieval of Physiological Signals via Attribute-specific Clinical Prototypes
The ongoing digitization of health records within the healthcare industry results in large-scale datasets.
CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients
This data can be exploited via contrastive learning, a self-supervised pre-training method that encourages representations of instances to be similar to one another.
ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks
Keywords: entropy minimisation, maximum entropy, confidence penalty, self knowledge distillation, label correction, label noise, semi-supervised learning, output regularisation
SoQal: Selective Oracle Questioning in Active Learning
Large sets of unlabelled data within the healthcare domain remain underutilized.
CLOPS: Continual Learning of Physiological Signals
Deep learning algorithms are known to experience destructive interference when instances violate the assumption of being independent and identically distributed (i. i. d).
SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac Signals
One way to mitigate this burden is via active learning (AL) which involves the (a) acquisition and (b) annotation of informative unlabelled instances.
Severity Detection Tool for Patients with Infectious Disease
A support vector machine is employed to classify the ANSD levels.
Preserving Patient Privacy while Training a Predictive Model of In-hospital Mortality
Machine learning models can be used for pattern recognition in medical data in order to improve patient outcomes, such as the prediction of in-hospital mortality.
Fusing Continuous-valued Medical Labels using a Bayesian Model
To address these problems, a Bayesian Continuous-valued Label Aggregator(BCLA) is proposed to provide a reliable estimation of label aggregation while accurately infer the precision and bias of each algorithm.
