AIMC Topic: Supervised Machine Learning

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Hierarchical progressive learning of cell identities in single-cell data.

Nature communications
Supervised methods are increasingly used to identify cell populations in single-cell data. Yet, current methods are limited in their ability to learn from multiple datasets simultaneously, are hampered by the annotation of datasets at different resol...

Weakly Supervised Histopathology Image Segmentation With Sparse Point Annotations.

IEEE journal of biomedical and health informatics
Digital histopathology image segmentation can facilitate computer-assisted cancer diagnostics. Given the difficulty of obtaining manual annotations, weak supervision is more suitable for the task than full supervision is. However, most weakly supervi...

Combining weakly and strongly supervised learning improves strong supervision in Gleason pattern classification.

BMC medical imaging
BACKGROUND: One challenge to train deep convolutional neural network (CNNs) models with whole slide images (WSIs) is providing the required large number of costly, manually annotated image regions. Strategies to alleviate the scarcity of annotated da...

Domain adaptation and self-supervised learning for surgical margin detection.

International journal of computer assisted radiology and surgery
PURPOSE: One in five women who undergo breast conserving surgery will need a second revision surgery due to remaining tumor. The iKnife is a mass spectrometry modality that produces real-time margin information based on the metabolite signatures in s...

Flexible multi-view semi-supervised learning with unified graph.

Neural networks : the official journal of the International Neural Network Society
At present, the diversity of data acquisition boosts the growth of multi-view data and the lack of label information. Since manually labeling is expensive and impractical, it is practical to enhance learning performance with a small amount of labeled...

Regression plane concept for analysing continuous cellular processes with machine learning.

Nature communications
Biological processes are inherently continuous, and the chance of phenotypic discovery is significantly restricted by discretising them. Using multi-parametric active regression we introduce the Regression Plane (RP), a user-friendly discovery tool e...

Machine learning based models for prediction of subtype diagnosis of primary aldosteronism using blood test.

Scientific reports
Primary aldosteronism (PA) is associated with an increased risk of cardiometabolic diseases, especially in unilateral subtype. Despite its high prevalence, the case detection rate of PA is limited, partly because of no clinical models available in ge...

Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology.

Molecules (Basel, Switzerland)
Oral bioavailability (F) is an essential determinant for the systemic exposure and dosing regimens of drug candidates. F is determined by numerous processes, and computational predictions of human estimates have so far shown limited results. We descr...

Autonomous Construction of Phase Diagrams of Block Copolymers by Theory-Assisted Active Machine Learning.

ACS macro letters
Equilibrium phase diagrams serve as blueprints for rational design of nanostructured materials of block copolymers, but their construction is time-consuming and requires profound expertise. Herein, by virtue of the knowledge of self-consistent field ...