AIMC Topic: Machine Learning

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Generalized robust loss functions for machine learning.

Neural networks : the official journal of the International Neural Network Society
Loss function is a critical component of machine learning. Some robust loss functions are proposed to mitigate the adverse effects caused by noise. However, they still face many challenges. Firstly, there is currently a lack of unified frameworks for...

Machine learning application in autoimmune diseases: State of art and future prospectives.

Autoimmunity reviews
Autoimmune diseases are a group of disorders resulting from an alteration of immune tolerance, characterized by the formation of autoantibodies and the consequent development of heterogeneous clinical manifestations. Diagnosing autoimmune diseases is...

Automatic detection of ischemic necrotic sites in small intestinal tissue using hyperspectral imaging and transfer learning.

Journal of biophotonics
Acquiring large amounts of hyperspectral data of small intestinal tissue with real labels in the clinic is difficult, and the data shows inter-patient variability. Building an automatic identification model using a small dataset presents a crucial ch...

Computational and artificial intelligence-based approaches for drug metabolism and transport prediction.

Trends in pharmacological sciences
Drug metabolism and transport, orchestrated by drug-metabolizing enzymes (DMEs) and drug transporters (DTs), are implicated in drug-drug interactions (DDIs) and adverse drug reactions (ADRs). Reliable and precise predictions of DDIs and ADRs are crit...

Editorial Topical Collection: "Explainable and Augmented Machine Learning for Biosignals and Biomedical Images".

Sensors (Basel, Switzerland)
Machine learning (ML) is a well-known subfield of artificial intelligence (AI) that aims at developing algorithms and statistical models able to empower computer systems to automatically adapt to a specific task through experience or learning from da...

Rapid detection of colored and colorless macro- and micro-plastics in complex environment via near-infrared spectroscopy and machine learning.

Journal of environmental sciences (China)
To better understand the migration behavior of plastic fragments in the environment, development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary. However, most of the studies had focu...

Advancing chronic toxicity risk assessment in freshwater ecology by molecular characterization-based machine learning.

Environmental pollution (Barking, Essex : 1987)
The continuously increased production of various chemicals and their release into environments have raised potential negative effects on ecological health. However, traditional labor-intensive assessment methods cannot effectively and rapidly evaluat...

A machine learning approach toward automating spatial identification of LAG3+/CD3+ cells in ulcerative colitis.

Scientific reports
Over the past decade, automation of digital image analysis has become commonplace in both research and clinical settings. Spurred by recent advances in artificial intelligence and machine learning (AI/ML), tissue sub-compartments and cellular phenoty...