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Supervised Machine Learning

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Tailoring pretext tasks to improve self-supervised learning in histopathologic subtype classification of lung adenocarcinomas.

Computers in biology and medicine
Lung adenocarcinoma (LUAD) is a morphologically heterogeneous disease with five predominant histologic subtypes. Fully supervised convolutional neural networks can improve the accuracy and reduce the subjectivity of LUAD histologic subtyping using he...

Supervised learning algorithm for analysis of communication signals in the weakly electric fish Apteronotus leptorhynchus.

Journal of comparative physiology. A, Neuroethology, sensory, neural, and behavioral physiology
Signal analysis plays a preeminent role in neuroethological research. Traditionally, signal identification has been based on pre-defined signal (sub-)types, thus being subject to the investigator's bias. To address this deficiency, we have developed ...

A foundation model for generalizable disease detection from retinal images.

Nature
Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis of eye diseases and systemic disorders. However, the development of AI models requires substantial a...

Confidence-guided mask learning for semi-supervised medical image segmentation.

Computers in biology and medicine
Semi-supervised learning aims to train a high-performance model with a minority of labeled data and a majority of unlabeled data. Existing methods mostly adopt the mechanism of prediction task to obtain precise segmentation maps with the constraints ...

Mitigating underreported error in food frequency questionnaire data using a supervised machine learning method and error adjustment algorithm.

BMC medical informatics and decision making
BACKGROUND: Food frequency questionnaires (FFQs) are one of the most useful tools for studying and understanding diet-disease relationships. However, because FFQs are self-reported data, they are susceptible to response bias, social desirability bias...

An Efficient and Private ECG Classification System Using Split and Semi-Supervised Learning.

IEEE journal of biomedical and health informatics
Electrocardiography (ECG) is a standard diagnostic tool for evaluating the overall heart's electrical activity and is vital for detecting many cardiovascular diseases. Classifying ECG recordings using deep neural networks has been investigated in lit...

Fundus Image-Label Pairs Synthesis and Retinopathy Screening via GANs With Class-Imbalanced Semi-Supervised Learning.

IEEE transactions on medical imaging
Retinopathy is the primary cause of irreversible yet preventable blindness. Numerous deep-learning algorithms have been developed for automatic retinal fundus image analysis. However, existing methods are usually data-driven, which rarely consider th...

SCANet: A Unified Semi-Supervised Learning Framework for Vessel Segmentation.

IEEE transactions on medical imaging
Automatic subcutaneous vessel imaging with near-infrared (NIR) optical apparatus can promote the accuracy of locating blood vessels, thus significantly contributing to clinical venipuncture research. Though deep learning models have achieved remarkab...

Self-Supervised Learning for Annotation Efficient Biomedical Image Segmentation.

IEEE transactions on bio-medical engineering
OBJECTIVE: The scarcity of high-quality annotated data is omnipresent in machine learning. Especially in biomedical segmentation applications, experts need to spend a lot of their time into annotating due to the complexity. Hence, methods to reduce s...

SentiUrdu-1M: A large-scale tweet dataset for Urdu text sentiment analysis using weakly supervised learning.

PloS one
Low-resource languages are gaining much-needed attention with the advent of deep learning models and pre-trained word embedding. Though spoken by more than 230 million people worldwide, Urdu is one such low-resource language that has recently gained ...