AI Medical Compendium Topic:
Supervised Machine Learning

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Centroid Estimation With Guaranteed Efficiency: A General Framework for Weakly Supervised Learning.

IEEE transactions on pattern analysis and machine intelligence
In this paper, we propose a general framework termed centroid estimation with guaranteed efficiency (CEGE) for weakly supervised learning (WSL) with incomplete, inexact, and inaccurate supervision. The core of our framework is to devise an unbiased a...

Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology.

Medical image analysis
Artificial intelligence (AI) can extract visual information from histopathological slides and yield biological insight and clinical biomarkers. Whole slide images are cut into thousands of tiles and classification problems are often weakly-supervised...

A Hybrid Approach for Approximating the Ideal Observer for Joint Signal Detection and Estimation Tasks by Use of Supervised Learning and Markov-Chain Monte Carlo Methods.

IEEE transactions on medical imaging
The ideal observer (IO) sets an upper performance limit among all observers and has been advocated for assessing and optimizing imaging systems. For general joint detection and estimation (detection-estimation) tasks, estimation ROC (EROC) analysis h...

Endoscopy image enhancement method by generalized imaging defect models based adversarial training.

Physics in medicine and biology
Smoke, uneven lighting, and color deviation are common issues in endoscopic surgery, which have increased the risk of surgery and even lead to failure.In this study, we present a new physics model driven semi-supervised learning framework for high-qu...

Self-supervised learning via cluster distance prediction for operating room context awareness.

International journal of computer assisted radiology and surgery
PURPOSE: Semantic segmentation and activity classification are key components to create intelligent surgical systems able to understand and assist clinical workflow. In the operating room, semantic segmentation is at the core of creating robots aware...

Discriminative error prediction network for semi-supervised colon gland segmentation.

Medical image analysis
Pixel-wise error correction of initial segmentation results provides an effective way for quality improvement. The additional error segmentation network learns to identify correct predictions and incorrect ones. The performance on error segmentation ...

Supervised and semi-supervised 3D organ localisation in CT images combining reinforcement learning with imitation learning.

Biomedical physics & engineering express
Computer aided diagnostics often requires analysis of a region of interest (ROI) within a radiology scan, and the ROI may be an organ or a suborgan. Although deep learning algorithms have the ability to outperform other methods, they rely on the avai...

Prototype Regularized Manifold Regularization Technique for Semi-Supervised Online Extreme Learning Machine.

Sensors (Basel, Switzerland)
Data streaming applications such as the Internet of Things (IoT) require processing or predicting from sequential data from various sensors. However, most of the data are unlabeled, making applying fully supervised learning algorithms impossible. The...

TSRNet: Diagnosis of COVID-19 based on self-supervised learning and hybrid ensemble model.

Computers in biology and medicine
BACKGROUND: As of Feb 27, 2022, coronavirus (COVID-19) has caused 434,888,591 infections and 5,958,849 deaths worldwide, dealing a severe blow to the economies and cultures of most countries around the world. As the virus has mutated, its infectious ...

Simple, fast, and flexible framework for matrix completion with infinite width neural networks.

Proceedings of the National Academy of Sciences of the United States of America
Matrix completion problems arise in many applications including recommendation systems, computer vision, and genomics. Increasingly larger neural networks have been successful in many of these applications but at considerable computational costs. Rem...