AIMC Topic: Neural Networks, Computer

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Development and validation of a deep learning system for ascites cytopathology interpretation.

Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
BACKGROUND: Early diagnosis of Peritoneal metastasis (PM) is clinically significant regarding optimal treatment selection and avoidance of unnecessary surgical procedures. Cytopathology plays an important role in early screening of PM. We aimed to de...

Implementation of model explainability for a basic brain tumor detection using convolutional neural networks on MRI slices.

Neuroradiology
PURPOSE: While neural networks gain popularity in medical research, attempts to make the decisions of a model explainable are often only made towards the end of the development process once a high predictive accuracy has been achieved.

Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach.

Clinical EEG and neuroscience
The human brain is characterized by complex structural, functional connections that integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation of both structural and functional connections of the brain and the effect...

DeepDTI: High-fidelity six-direction diffusion tensor imaging using deep learning.

NeuroImage
Diffusion tensor magnetic resonance imaging (DTI) is unsurpassed in its ability to map tissue microstructure and structural connectivity in the living human brain. Nonetheless, the angular sampling requirement for DTI leads to long scan times and pos...

Evaluation of Deep Neural Networks for Semantic Segmentation of Prostate in T2W MRI.

Sensors (Basel, Switzerland)
In this paper, we present an evaluation of four encoder-decoder CNNs in the segmentation of the prostate gland in T2W magnetic resonance imaging (MRI) image. The four selected CNNs are FCN, SegNet, U-Net, and DeepLabV3+, which was originally proposed...

Particulate impurities in cell-based medicinal products traced by flow imaging microscopy combined with deep learning for image analysis.

Cytotherapy
Cell-based medicinal products (CBMPs) are rapidly gaining importance in the treatment of life-threatening diseases. However, the analytical toolbox for characterization of CBMPs is limited. The aim of our study was to develop a method based on flow i...

Prediction of N6-methyladenosine sites using convolution neural network model based on distributed feature representations.

Neural networks : the official journal of the International Neural Network Society
N-methyladenosine (mA) is a well-studied and most common interior messenger RNA (mRNA) modification that plays an important function in cell development. NA is found in all kingdoms​ of life and many other cellular processes such as RNA splicing, imm...

Image style transfer with collection representation space and semantic-guided reconstruction.

Neural networks : the official journal of the International Neural Network Society
Image style transfer renders the content of an image into different styles. Current methods made decent progress with transferring the style of single image, however, visual statistics from one image cannot reflect the full scope of an artist. Also, ...

Computer-aided diagnosis for fetal brain ultrasound images using deep convolutional neural networks.

International journal of computer assisted radiology and surgery
PURPOSE: Fetal brain abnormalities are some of the most common congenital malformations that may associated with syndromic and chromosomal malformations, and could lead to neurodevelopmental delay and mental retardation. Early prenatal detection of b...