AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Supervised Machine Learning

Showing 561 to 570 of 1605 articles

Clear Filters

Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks.

Molecules (Basel, Switzerland)
The classification of biological neuron types and networks poses challenges to the full understanding of the human brain's organisation and functioning. In this paper, we develop a novel objective classification model of biological neuronal morpholog...

Self-supervised graph neural network with pre-training generative learning for recommendation systems.

Scientific reports
The case assignment system is an essential system of case management and assignment within the procuratorate and is an important aspect of judicial fairness and efficiency. However, existing methods mostly use manual or random case assignment, which ...

Development of early prediction model for pregnancy-associated hypertension with graph-based semi-supervised learning.

Scientific reports
Clinical guidelines recommend several risk factors to identify women in early pregnancy at high risk of developing pregnancy-associated hypertension. However, these variables result in low predictive accuracy. Here, we developed a prediction model fo...

Gait Recognition with Self-Supervised Learning of Gait Features Based on Vision Transformers.

Sensors (Basel, Switzerland)
Gait is a unique biometric trait with several useful properties. It can be recognized remotely and without the cooperation of the individual, with low-resolution cameras, and it is difficult to obscure. Therefore, it is suitable for crime investigati...

Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning.

Nature biomedical engineering
In tasks involving the interpretation of medical images, suitably trained machine-learning models often exceed the performance of medical experts. Yet such a high-level of performance typically requires that the models be trained with relevant datase...

ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning.

IEEE transactions on pattern analysis and machine intelligence
Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we ...

Fast Weakly Supervised Action Segmentation Using Mutual Consistency.

IEEE transactions on pattern analysis and machine intelligence
Action segmentation is the task of predicting the actions for each frame of a video. As obtaining the full annotation of videos for action segmentation is expensive, weakly supervised approaches that can learn only from transcripts are appealing. In ...

A Concise Yet Effective Model for Non-Aligned Incomplete Multi-View and Missing Multi-Label Learning.

IEEE transactions on pattern analysis and machine intelligence
In reality, learning from multi-view multi-label data inevitably confronts three challenges: missing labels, incomplete views, and non-aligned views. Existing methods mainly concern the first two and commonly need multiple assumptions to attack them,...

Improving Chemical Reaction Prediction with Unlabeled Data.

Molecules (Basel, Switzerland)
Predicting products of organic chemical reactions is useful in chemical sciences, especially when one or more reactants are new organics. However, the performance of traditional learning models heavily relies on high-quality labeled data. In this wor...

Multi-Agent Multi-View Collaborative Perception Based on Semi-Supervised Online Evolutive Learning.

Sensors (Basel, Switzerland)
In the edge intelligence environment, multiple sensing devices perceive and recognize the current scene in real time to provide specific user services. However, the generalizability of the fixed recognition model will gradually weaken due to the time...