no code implementations • 2 Sep 2023 • Fengxiang Bie, Yibo Yang, Zhongzhu Zhou, Adam Ghanem, Minjia Zhang, Zhewei Yao, Xiaoxia Wu, Connor Holmes, Pareesa Golnari, David A. Clifton, Yuxiong He, DaCheng Tao, Shuaiwen Leon Song
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.
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.
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.
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.
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.
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.
Electronic Health Records (EHRs) contain sensitive patient information, which presents privacy concerns when sharing such data.
To our knowledge, this is the first comprehensive study specifically focused on creating efficient and compact transformers for clinical NLP tasks.
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.
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)
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.
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.
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.
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.
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 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.
A particular challenge for disease progression modeling is the heterogeneity of a disease and its manifestations in the patients.
For the issue (2), the effectiveness of ProSelfLC defends entropy minimisation.
Advances in deep learning for human activity recognition have been relatively limited due to the lack of large labelled datasets.
With the rapid growth of memory and computing power, datasets are becoming increasingly complex and imbalanced.
"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.
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.
Additionally, CMD covers two special cases: zero-knowledge and all knowledge, leading to a unified MKD framework.
The ubiquity and rate of collection of physiological signals produce large, unlabelled datasets.
The process of manually searching for relevant instances in, and extracting information from, clinical databases underpin a multitude of clinical tasks.
The ongoing digitization of health records within the healthcare industry results in large-scale datasets.
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.
Keywords: entropy minimisation, maximum entropy, confidence penalty, self knowledge distillation, label correction, label noise, semi-supervised learning, output regularisation
Deep learning algorithms are known to experience destructive interference when instances violate the assumption of being independent and identically distributed (i. i. d).
One way to mitigate this burden is via active learning (AL) which involves the (a) acquisition and (b) annotation of informative unlabelled instances.
no code implementations • 10 Dec 2019 • Girmaw Abebe Tadesse, Tingting Zhu, Nhan Le Nguyen Thanh, Nguyen Thanh Hung, Ha Thi Hai Duong, Truong Huu Khanh, Pham Van Quang, Duc Duong Tran, LamMinh Yen, H Rogier Van Doorn, Nguyen Van Hao, John Prince, Hamza Javed, DaniKiyasseh, Le Van Tan, Louise Thwaites, David A. Clifton
A support vector machine is employed to classify the ANSD levels.
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.
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.