AIMC Topic: Neural Networks, Computer

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Transfer learning may explain pigeons' ability to detect cancer in histopathology.

Bioinspiration & biomimetics
Pigeons' unexpected competence in learning to categorize unseen histopathological images has remained an unexplained discovery for almost a decade (Levenson2015e0141357). Could it be that knowledge transferred from their bird's-eye views of the earth...

An efficient channel recurrent Criss-cross attention network for epileptic seizure prediction.

Medical engineering & physics
Epilepsy is a chronic disease caused by repeated abnormal discharge of neurons in the brain. Accurately predicting the onset of epilepsy can effectively improve the quality of life for patients with the condition. While there are many methods for det...

Convolutional neural networks can identify brain interactions involved in decoding spatial auditory attention.

PLoS computational biology
Human listeners have the ability to direct their attention to a single speaker in a multi-talker environment. The neural correlates of selective attention can be decoded from a single trial of electroencephalography (EEG) data. In this study, leverag...

Period-aggregated transformer for learning latent seasonalities in long-horizon financial time series.

PloS one
Fluctuations in the financial market are influenced by various driving forces and numerous factors. Traditional financial research aims to identify the factors influencing stock prices, and existing works construct a common neural network learning fr...

Detection of Lungs Tumors in CT Scan Images Using Convolutional Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Current human being's lifestyle has caused / exacerbated many diseases. One of these diseases is cancer, and among all kinds of cancers like, brain pulmonary; lung cancer is fatal. The cancers could be detected early to save lives using Computer Aide...

Hierarchical Hybrid Networks for Automatic Pulmonary Blood Vessel Segmentation in Computed Tomography Images.

IEEE/ACM transactions on computational biology and bioinformatics
Pulmonary arterial hypertension (PAH) is considered the third most common cardiovascular disease after coronary heart disease and hypertension. The diagnosis of PAH is mainly based on the comprehensive judgment of computed tomography and other medica...

Big Data Analytics on Lung Cancer Diagnosis Framework With Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics
As the segment of diseased tissue in PET images is time-consuming, laborious and low accuracy, this work proposes an automated framework for PET image screening, denoising and diseased tissue segmentation. First, taking into account the characteristi...

SSP-Net: A Siamese-Based Structure-Preserving Generative Adversarial Network for Unpaired Medical Image Enhancement.

IEEE/ACM transactions on computational biology and bioinformatics
Recently, unpaired medical image enhancement is one of the important topics in medical research. Although deep learning-based methods have achieved remarkable success in medical image enhancement, such methods face the challenge of low-quality traini...

Robust and Privacy-Preserving Decentralized Deep Federated Learning Training: Focusing on Digital Healthcare Applications.

IEEE/ACM transactions on computational biology and bioinformatics
Federated learning of deep neural networks has emerged as an evolving paradigm for distributed machine learning, gaining widespread attention due to its ability to update parameters without collecting raw data from users, especially in digital health...

Deep Factor Learning for Accurate Brain Neuroimaging Data Analysis on Discrimination for Structural MRI and Functional MRI.

IEEE/ACM transactions on computational biology and bioinformatics
Analysis of neuroimaging data (e.g., Magnetic Resonance Imaging, structural and functional MRI) plays an important role in monitoring brain dynamics and probing brain structures. Neuroimaging data are multi-featured and non-linear by nature, and it i...