Erratum: Automatic classification and neurotransmitter prediction of synapses in electron microscopy - CORRIGENDUM.

[This corrects the article DOI: 10.1017/S2633903X2200006X.].

Automatic classification and neurotransmitter prediction of synapses in electron microscopy.

This paper presents a deep-learning-based workflow to detect synapses and predict their neurotransmitter type in the primitive chordate () electron microscopic (EM) images. Identifying synapses from EM images to build a full map of connections between neurons is a labor-intensive process and requires significant domain expertise. Automation of synapse classification would hasten the generation and analysis of connectomes.

Fault-Tolerant Sensor Detection of sEMG signals: Quality Analysis Using a Two-Class Support Vector Machine.

The capacity to identify the contamination in surface electromyography (sEMG) signals is necessary for applying the sEMG controlled prosthesis over time. In this paper, the method for the automatic identification of commonly occurring contaminant types in sEMG signals is evaluated. The presented approach uses two-class support vector machine (SVM) trained with clean sEMG and artificially contaminated sEMG.

A Framework for Automatic Recognition of Cell Damage on Microscopic Images using Artificial Neural Networks.

Despite several technological advances in the past years, the vast majority of microscopy examinations continue to be performed in a very laborious, time-consuming manner, requiring highly experienced personnel to spend several hours to visually examine each microscope slide. Due to recent improvements in modern Digital Image Processing, professionals that work on microscopic exams could benefit from new tools that can apply image processing possibilities to their specific field. We propose a framework consisting of an image segmentation stage, feature extraction, and then a Shallow Neural Network related to human perception.