AIMC Topic: Neural Networks, Computer

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Methods for cell-type annotation on scRNA-seq data: A recent overview.

Journal of bioinformatics and computational biology
The evolution of single-cell technology is ongoing, continually generating massive amounts of data that reveal many mysteries surrounding intricate diseases. However, their drawbacks continue to constrain us. Among these, annotating cell types in sin...

A Deep Learning Model for Correlation Analysis between Electroencephalography Signal and Speech Stimuli.

Sensors (Basel, Switzerland)
In recent years, the use of electroencephalography (EEG) has grown as a tool for diagnostic and brain function monitoring, being a simple and non-invasive method compared with other procedures like histological sampling. Typically, in order to extrac...

Classification of breast lesions in ultrasound images using deep convolutional neural networks: transfer learning versus automatic architecture design.

Medical & biological engineering & computing
Deep convolutional neural networks (DCNNs) have demonstrated promising performance in classifying breast lesions in 2D ultrasound (US) images. Exiting approaches typically use pre-trained models based on architectures designed for natural images with...

Backdoor attack and defense in federated generative adversarial network-based medical image synthesis.

Medical image analysis
Deep Learning-based image synthesis techniques have been applied in healthcare research for generating medical images to support open research and augment medical datasets. Training generative adversarial neural networks (GANs) usually require large ...

Understanding calibration of deep neural networks for medical image classification.

Computer methods and programs in biomedicine
Background and Objective - In the field of medical image analysis, achieving high accuracy is not enough; ensuring well-calibrated predictions is also crucial. Confidence scores of a deep neural network play a pivotal role in explainability by provid...

Understanding neural network through neuron level visualization.

Neural networks : the official journal of the International Neural Network Society
Neurons are the fundamental units of neural networks. In this paper, we propose a method for explaining neural networks by visualizing the learning process of neurons. For a trained neural network, the proposed method obtains the features learned by ...

Boosted Additive Angular Margin Loss for breast cancer diagnosis from histopathological images.

Computers in biology and medicine
Pathologists use biopsies and microscopic examination to accurately diagnose breast cancer. This process is time-consuming, labor-intensive, and costly. Convolutional neural networks (CNNs) offer an efficient and highly accurate approach to reduce an...

A new architecture combining convolutional and transformer-based networks for automatic 3D multi-organ segmentation on CT images.

Medical physics
PURPOSE: Deep learning-based networks have become increasingly popular in the field of medical image segmentation. The purpose of this research was to develop and optimize a new architecture for automatic segmentation of the prostate gland and normal...

DASH: Dynamic Attention-Based Substructure Hierarchy for Partial Charge Assignment.

Journal of chemical information and modeling
We present a robust and computationally efficient approach for assigning partial charges of atoms in molecules. The method is based on a hierarchical tree constructed from attention values extracted from a graph neural network (GNN), which was traine...

MBT3D: Deep learning based multi-object tracker for bumblebee 3D flight path estimation.

PloS one
This work presents the Multi-Bees-Tracker (MBT3D) algorithm, a Python framework implementing a deep association tracker for Tracking-By-Detection, to address the challenging task of tracking flight paths of bumblebees in a social group. While trackin...