AIMC Topic: Neural Networks, Computer

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A convolutional attention mapping deep neural network for classification and localization of cardiomegaly on chest X-rays.

Scientific reports
Building a reliable and precise model for disease classification and identifying abnormal sites can provide physicians assistance in their decision-making process. Deep learning based image analysis is a promising technique for enriching the decision...

Brain Tumor Classification Using Deep Neural Network and Transfer Learning.

Brain topography
In the field of medical imaging, the classification of brain tumors based on histopathological analysis is a laborious and traditional approach. To address this issue, the use of deep learning techniques, specifically Convolutional Neural Networks (C...

SYNDEEP: a deep learning approach for the prediction of cancer drugs synergy.

Scientific reports
Drug combinations can be the prime strategy for increasing the initial treatment options in cancer therapy. However, identifying the combinations through experimental approaches is very laborious and costly. Notably, in vitro and/or in vivo examinati...

Identification of thrombopoiesis inducer based on a hybrid deep neural network model.

Thrombosis research
Thrombocytopenia is a common haematological problem worldwide. Currently, there are no relatively safe and effective agents for the treatment of thrombocytopenia. To address this challenge, we propose a computational method that enables the discovery...

Multifunctional Optoelectronic Synapses Based on Arrayed MoS Monolayers Emulating Human Association Memory.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Optoelectronic synaptic devices integrating light-perception and signal-storage functions hold great potential in neuromorphic computing for visual information processing, as well as complex brain-like learning, memorizing, and reasoning. Herein, the...

Experimental Exploration of Multilevel Human Pain Assessment Using Blood Volume Pulse (BVP) Signals.

Sensors (Basel, Switzerland)
Critically ill patients often lack cognitive or communicative functions, making it challenging to assess their pain levels using self-reporting mechanisms. There is an urgent need for an accurate system that can assess pain levels without relying on ...

Segmentation of thyroid glands and nodules in ultrasound images using the improved U-Net architecture.

BMC medical imaging
BACKGROUND: Identifying thyroid nodules' boundaries is crucial for making an accurate clinical assessment. However, manual segmentation is time-consuming. This paper utilized U-Net and its improved methods to automatically segment thyroid nodules and...

Automatic sleep staging for the young and the old - Evaluating age bias in deep learning.

Sleep medicine
BACKGROUND: Various deep-learning systems have been proposed for automated sleep staging. Still, the significance of age-specific underrepresentation in training data and the resulting errors in clinically used sleep metrics are unknown.

Morphological feature recognition of different differentiation stages of induced ADSCs based on deep learning.

Computers in biology and medicine
In order to accurately identify the morphological features of different differentiation stages of induced Adipose Derived Stem Cells (ADSCs) and judge the differentiation types of induced ADSCs, a morphological feature recognition method of different...

Spectral Classification of Large-Scale Blended (Micro)Plastics Using FT-IR Raw Spectra and Image-Based Machine Learning.

Environmental science & technology
Microplastics (MPs) are currently recognized as emerging pollutants; their identification and classification are therefore essential during their monitoring and management. In contrast to most studies based on small datasets and library searches, thi...