AIMC Topic: Machine Learning

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MIRIAM: A machine and deep learning single-cell segmentation and quantification pipeline for multi-dimensional tissue images.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Increasingly, highly multiplexed tissue imaging methods are used to profile protein expression at the single-cell level. However, a critical limitation is the lack of robust cell segmentation tools for tissue sections. We present Multiplexed Image Re...

Selection of diagnosis with oncologic relevance information from histopathology free text reports: A machine learning approach.

International journal of medical informatics
Histopathology reports are a primary data source for the case definition phase of a Cancer Registry. By reading the histopathology report, the operator that evaluates an oncology case can define the morphology and topography of cancer, and validate t...

Machine learning based predictive model for the analysis of sequence activity relationships using protein spectra and protein descriptors.

Journal of biomedical informatics
Accurately establishing the connection between a protein sequence and its function remains a focal point within the field of protein engineering, especially in the context of predicting the effects of mutations. From this, there has been a continued ...

[Forecasts from the retort. A Greek gift of artificial intelligence : Interdisciplinary data analysis in preoperative imaging diagnostics].

Der Chirurg; Zeitschrift fur alle Gebiete der operativen Medizen
The growing influence of artificial intelligence on radiology not only leads to a fundamental change in the way diagnoses are made but also creates a wealth of additional information. Many programs correlate the parameters of image evaluation with th...

De Novo Peptide and Protein Design Using Generative Adversarial Networks: An Update.

Journal of chemical information and modeling
Nowadays, machine learning and deep learning approaches are widely utilized for generative chemistry and computer-aided drug design and discovery such as de novo peptide and protein design, where target-specific peptide-based/protein-based therapeuti...

A pilot study for the prediction of liver function related scores using breath biomarkers and machine learning.

Scientific reports
Volatile organic compounds (VOCs) present in exhaled breath can help in analysing biochemical processes in the human body. Liver diseases can be traced using VOCs as biomarkers for physiological and pathophysiological conditions. In this work, we pro...

Investigating the Role of Image Fusion in Brain Tumor Classification Models Based on Machine Learning Algorithm for Personalized Medicine.

Computational and mathematical methods in medicine
Image fusion can be performed on images either in spatial domain or frequency domain methods. Frequency domain methods will be most preferred because these methods can improve the quality of edges in an image. In image fusion, the resultant fused ima...

Enabling CT-Scans for covid detection using transfer learning-based neural networks.

Journal of biomolecular structure & dynamics
Today, we are coping with the pandemic, and the novel virus is covertly evolving day by day. Therefore, a precautionary system to deal with the issue is required as early as possible. The last few years were very challenging for doctors, vaccine make...

Ultrathin Eardrum-Inspired Self-Powered Acoustic Sensor for Vocal Synchronization Recognition with the Assistance of Machine Learning.

Small (Weinheim an der Bergstrasse, Germany)
With the rapid development of human-machine interfaces, artificial acoustic sensors play an important role in the hearing impaired. Here, an ultrathin eardrum-like triboelectric acoustic sensor (ETAS) is presented consisting of silver-coated nanofibe...

Vibration-Based Loosening Detection of a Multi-Bolt Structure Using Machine Learning Algorithms.

Sensors (Basel, Switzerland)
Since artificial intelligence (AI) was introduced into engineering fields, it has made many breakthroughs. Machine learning (ML) algorithms have been very commonly used in structural health monitoring (SHM) systems in the last decade. In this study, ...