AI Medical Compendium Topic

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Supervised Machine Learning

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Semi-supervised learning for topographic map analysis over time: a study of bridge segmentation.

Scientific reports
Geographical research using historical maps has progressed considerably as the digitalization of topological maps across years provides valuable data and the advancement of AI machine learning models provides powerful analytic tools. Nevertheless, an...

A Unified Framework for Automatic Distributed Active Learning.

IEEE transactions on pattern analysis and machine intelligence
We propose a novel unified frameork for automated distributed active learning (AutoDAL) to address multiple challenging problems in active learning such as limited labeled data, imbalanced datasets, automatic hyperparameter selection as well as scala...

Self-Supervised Human Detection and Segmentation via Background Inpainting.

IEEE transactions on pattern analysis and machine intelligence
While supervised object detection and segmentation methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained on. To address this when annotating data is prohibiti...

Neurodynamics-driven holistic approaches to semi-supervised feature selection.

Neural networks : the official journal of the International Neural Network Society
Feature selection is a crucial part of machine learning and pattern recognition, which aims at selecting a subset of informative features from the original dataset. Because of label information, supervised feature selection performs better than unsup...

Self-supervised machine learning for live cell imagery segmentation.

Communications biology
Segmenting single cells is a necessary process for extracting quantitative data from biological microscopy imagery. The past decade has seen the advent of machine learning (ML) methods to aid in this process, the overwhelming majority of which fall u...

Semi-Supervised Neuron Segmentation via Reinforced Consistency Learning.

IEEE transactions on medical imaging
Emerging deep learning-based methods have enabled great progress in automatic neuron segmentation from Electron Microscopy (EM) volumes. However, the success of existing methods is heavily reliant upon a large number of annotations that are often exp...

Complementary Memtransistor-Based Multilayer Neural Networks for Online Supervised Learning Through (Anti-)Spike-Timing-Dependent Plasticity.

IEEE transactions on neural networks and learning systems
We propose a complete hardware-based architecture of multilayer neural networks (MNNs), including electronic synapses, neurons, and periphery circuitry to implement supervised learning (SL) algorithm of extended remote supervised method (ReSuMe). In ...

Weakly supervised learning and interpretability for endometrial whole slide image diagnosis.

Experimental biology and medicine (Maywood, N.J.)
Fully supervised learning for whole slide image-based diagnostic tasks in histopathology is problematic due to the requirement for costly and time-consuming manual annotation by experts. Weakly supervised learning that utilizes only slide-level label...

Semi-Supervised Defect Detection Method with Data-Expanding Strategy for PCB Quality Inspection.

Sensors (Basel, Switzerland)
Printed circuit board (PCB) defect detection plays a crucial role in PCB production, and the popular methods are based on deep learning and require large-scale datasets with high-level ground-truth labels, in which it is time-consuming and costly to ...

Multi-modal contrastive mutual learning and pseudo-label re-learning for semi-supervised medical image segmentation.

Medical image analysis
Semi-supervised learning has a great potential in medical image segmentation tasks with a few labeled data, but most of them only consider single-modal data. The excellent characteristics of multi-modal data can improve the performance of semi-superv...