AIMC Topic: Neural Networks, Computer

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Evaluating feature extraction in ovarian cancer cell line co-cultures using deep neural networks.

Communications biology
Single-cell image analysis is crucial for studying drug effects on cellular morphology and phenotypic changes. Most studies focus on single cell types, overlooking the complexity of cellular interactions. Here, we establish an analysis pipeline to ex...

Enhancing E-commerce recommendations with sentiment analysis using MLA-EDTCNet and collaborative filtering.

Scientific reports
The rapid growth of e-commerce has made product recommendation systems essential for enhancing customer experience and driving business success. This research proposes an advanced recommendation framework that integrates sentiment analysis (SA) and c...

A feature explainability-based deep learning technique for diabetic foot ulcer identification.

Scientific reports
Diabetic foot ulcers (DFUs) are a common and serious complication of diabetes, presenting as open sores or wounds on the sole. They result from impaired blood circulation and neuropathy associated with diabetes, increasing the risk of severe infectio...

Development of weighted residual RNN model with hybrid heuristic algorithm for movement recognition framework in ambient assisted living.

Scientific reports
In healthcare applications, automatic and intelligent movement recognition systems in Ambient Assisted Living (AAL) are designed for elderly and disabled persons. The AAL provides assistance as well as secure feelings to disabled persons and elderly ...

RAE-Net: a multi-modal neural network based on feature fusion and evidential deep learning algorithm in predicting breast cancer subtypes on DCE-MRI.

Biomedical physics & engineering express
Accurate identification of molecular subtypes in breast cancer is critical for personalized treatment. This study introduces a novel neural network model, RAE-Net, based on Multimodal Feature Fusion (MFF) and the Evidential Deep Learning Algorithm (E...

Chinese medical named entity recognition utilizing entity association and gate context awareness.

PloS one
Recognizing medical named entities is a crucial aspect of applying deep learning in the medical domain. Automated methods for identifying specific entities from medical literature or other texts can enhance the efficiency and accuracy of information ...

Deep-DPC: Deep learning-assisted label-free temporal imaging discovery of anti-fibrotic compounds by controlling cell morphology.

Journal of advanced research
INTRODUCTION: Fibrosis can damage the normal function of many organs, such as cardiac function, for which no effective clinical therapies exist. However, traditional approaches to anti-fibrosis drug discovery have primarily focused on the final biolo...

MSTNet: Multi-scale spatial-aware transformer with multi-instance learning for diabetic retinopathy classification.

Medical image analysis
Diabetic retinopathy (DR), the leading cause of vision loss among diabetic adults worldwide, underscores the importance of early detection and timely treatment using fundus images to prevent vision loss. However, existing deep learning methods strugg...

Beyond human perception: challenges in AI interpretability of orangutan artwork.

Primates; journal of primatology
Drawings serve as a profound medium of expression for both humans and apes, offering unique insights into the cognitive and emotional landscapes of the artists, regardless of their species. This study employs artificial intelligence (AI), specificall...

Improving explanations for medical X-ray diagnosis combining variational autoencoders and adversarial machine learning.

Computers in biology and medicine
Explainability in Medical Computer Vision is one of the most sensible implementations of Artificial Intelligence nowadays in healthcare. In this work, we propose a novel Deep Learning architecture for eXplainable Artificial Intelligence, specially de...