AIMC Topic: Supervised Machine Learning

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Learning to Discover Explainable Clinical Features With Minimum Supervision.

Translational vision science & technology
PURPOSE: To compare supervised transfer learning to semisupervised learning for their ability to learn in-depth knowledge with limited data in the optical coherence tomography (OCT) domain.

Weakly Supervised Learning for Poorly Differentiated Adenocarcinoma Classification in GastricEndoscopic Submucosal Dissection Whole Slide Images.

Technology in cancer research & treatment
Endoscopic submucosal dissection (ESD) is the preferred technique for treating early gastric cancers including poorly differentiated adenocarcinoma without ulcerative findings. The histopathological classification of poorly differentiated adenocarci...

Ensemble of diverse deep neural networks with pseudo-labels for repayment prediction in social lending.

Science progress
In peer-to-peer (P2P) social lending, it is important to predict the repayment of borrowers. P2P lending data are generated in real-time, but most of them are pending to decide the repayment because the deadline is not yet expired. Adding the unexpir...

GRNUlar: A Deep Learning Framework for Recovering Single-Cell Gene Regulatory Networks.

Journal of computational biology : a journal of computational molecular cell biology
We propose GRNUlar, a novel deep learning framework for supervised learning of gene regulatory networks (GRNs) from single-cell RNA-Sequencing (scRNA-Seq) data. Our framework incorporates two intertwined models. First, we leverage the expressive abil...

Increasing the accuracy of single sequence prediction methods using a deep semi-supervised learning framework.

Bioinformatics (Oxford, England)
MOTIVATION: Over the past 50 years, our ability to model protein sequences with evolutionary information has progressed in leaps and bounds. However, even with the latest deep learning methods, the modelling of a critically important class of protein...

Learning Cellular Phenotypes through Supervision.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Image-based cell phenotyping is an important and open problem in computational pathology. The two principal challenges are: 1) making the cell cluster properties insensitive to experimental settings (like seed point and feature selection) and 2) ensu...

Hierarchical Consistency Regularized Mean Teacher for Semi-supervised 3D Left Atrium Segmentation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Deep learning has achieved promising segmentation performance on 3D left atrium MR images. However, annotations for segmentation tasks are expensive, costly and difficult to obtain. In this paper, we introduce a novel hierarchical consistency regular...

A new machine learning based user-friendly software platform for automatic radiomics modeling and analysis.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Supervised machine learning methods are usually used to build a custom model for disease diagnosis and auxiliary prognosis in radiomics studies. A classical machine learning pipeline involves a series of steps and multiple algorithms, which leads to ...