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Analyzing Overfitting Under Class Imbalance in Neural Networks for Image Segmentation.

IEEE transactions on medical imaging
Class imbalance poses a challenge for developing unbiased, accurate predictive models. In particular, in image segmentation neural networks may overfit to the foreground samples from small structures, which are often heavily under-represented in the ...

Convolutional neural network model based on radiological images to support COVID-19 diagnosis: Evaluating database biases.

PloS one
As SARS-CoV-2 has spread quickly throughout the world, the scientific community has spent major efforts on better understanding the characteristics of the virus and possible means to prevent, diagnose, and treat COVID-19. A valid approach presented i...

A fast saddle-point dynamical system approach to robust deep learning.

Neural networks : the official journal of the International Neural Network Society
Recent focus on robustness to adversarial attacks for deep neural networks produced a large variety of algorithms for training robust models. Most of the effective algorithms involve solving the min-max optimization problem for training robust models...

Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks.

Neural networks : the official journal of the International Neural Network Society
Unsupervised anomaly discovery in stream data is a research topic with many practical applications. However, in many cases, it is not easy to collect enough training data with labeled anomalies for supervised learning of an anomaly detector in order ...

COVID-19 in Iran: Forecasting Pandemic Using Deep Learning.

Computational and mathematical methods in medicine
COVID-19 has led to a pandemic, affecting almost all countries in a few months. In this work, we applied selected deep learning models including multilayer perceptron, random forest, and different versions of long short-term memory (LSTM), using thre...

Gaussian smoothing and modified histogram normalization methods to improve neural-biomarker interpretations for dyslexia classification mechanism.

PloS one
Achieving biologically interpretable neural-biomarkers and features from neuroimaging datasets is a challenging task in an MRI-based dyslexia study. This challenge becomes more pronounced when the needed MRI datasets are collected from multiple heter...

A deep learning-based system for bile duct annotation and station recognition in linear endoscopic ultrasound.

EBioMedicine
BACKGROUND: Detailed evaluation of bile duct (BD) is main focus during endoscopic ultrasound (EUS). The aim of this study was to develop a system for EUS BD scanning augmentation.

Multi-Path and Group-Loss-Based Network for Speech Emotion Recognition in Multi-Domain Datasets.

Sensors (Basel, Switzerland)
Speech emotion recognition (SER) is a natural method of recognizing individual emotions in everyday life. To distribute SER models to real-world applications, some key challenges must be overcome, such as the lack of datasets tagged with emotion labe...

JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Recently, the coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries, influencing billions of humans. To control the infection, identifying and separating the infected people is the most crucial step. The main diagnos...

Convergence of the RMSProp deep learning method with penalty for nonconvex optimization.

Neural networks : the official journal of the International Neural Network Society
A norm version of the RMSProp algorithm with penalty (termed RMSPropW) is introduced into the deep learning framework and its convergence is addressed both analytically and numerically. For rigour, we consider the general nonconvex setting and prove ...