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Supervised Machine Learning

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Explainable semi-supervised deep learning shows that dementia is associated with small, avocado-shaped clocks with irregularly placed hands.

Scientific reports
The clock drawing test is a simple and inexpensive method to screen for cognitive frailties, including dementia. In this study, we used the relevance factor variational autoencoder (RF-VAE), a deep generative neural network, to represent digitized cl...

Cancer Survival Prediction From Whole Slide Images With Self-Supervised Learning and Slide Consistency.

IEEE transactions on medical imaging
Histopathological Whole Slide Images (WSIs) at giga-pixel resolution are the gold standard for cancer analysis and prognosis. Due to the scarcity of pixel- or patch-level annotations of WSIs, many existing methods attempt to predict survival outcomes...

Semi-Supervised CT Lesion Segmentation Using Uncertainty-Based Data Pairing and SwapMix.

IEEE transactions on medical imaging
Semi-supervised learning (SSL) methods show their powerful performance to deal with the issue of data shortage in the field of medical image segmentation. However, existing SSL methods still suffer from the problem of unreliable predictions on unanno...

MuRCL: Multi-Instance Reinforcement Contrastive Learning for Whole Slide Image Classification.

IEEE transactions on medical imaging
Multi-instance learning (MIL) is widely adop- ted for automatic whole slide image (WSI) analysis and it usually consists of two stages, i.e., instance feature extraction and feature aggregation. However, due to the "weak supervision" of slide-level l...

Semi-Supervised Medical Image Segmentation Using Adversarial Consistency Learning and Dynamic Convolution Network.

IEEE transactions on medical imaging
Popular semi-supervised medical image segmentation networks often suffer from error supervision from unlabeled data since they usually use consistency learning under different data perturbations to regularize model training. These networks ignore the...

Weakly Supervised Classification of Vital Sign Alerts as Real or Artifact.

AMIA ... Annual Symposium proceedings. AMIA Symposium
A significant proportion of clinical physiologic monitoring alarms are false. This often leads to alarm fatigue in clinical personnel, inevitably compromising patient safety. To combat this issue, researchers have attempted to build Machine Learning ...

A multi-view co-training network for semi-supervised medical image-based prognostic prediction.

Neural networks : the official journal of the International Neural Network Society
Prognostic prediction has long been a hotspot in disease analysis and management, and the development of image-based prognostic prediction models has significant clinical implications for current personalized treatment strategies. The main challenge ...

Generalization of vision pre-trained models for histopathology.

Scientific reports
Out-of-distribution (OOD) generalization, especially for medical setups, is a key challenge in modern machine learning which has only recently received much attention. We investigate how different convolutional pre-trained models perform on OOD test ...

The Impact of Supervised Learning Methods in Ultralarge High-Throughput Docking.

Journal of chemical information and modeling
Structure-based virtual screening methods are, nowadays, one of the key pillars of computational drug discovery. In recent years, a series of studies have reported docking-based virtual screening campaigns of large databases ranging from hundreds to ...

Which Pixel to Annotate: A Label-Efficient Nuclei Segmentation Framework.

IEEE transactions on medical imaging
Recently deep neural networks, which require a large amount of annotated samples, have been widely applied in nuclei instance segmentation of H&E stained pathology images. However, it is inefficient and unnecessary to label all pixels for a dataset o...