AIMC Topic: Deep Learning

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Predictive models and applicability of artificial intelligence-based approaches in drug allergy.

Current opinion in allergy and clinical immunology
PURPOSE OF REVIEW: Drug allergy is responsible for a huge burden on public healthcare systems, representing in some instances a threat for patient's life. Diagnosis is complex due to the heterogeneity of clinical phenotypes and mechanisms involved, t...

Equivariant score-based generative diffusion framework for 3D molecules.

BMC bioinformatics
BACKGROUND: Molecular biology is crucial for drug discovery, protein design, and human health. Due to the vastness of the drug-like chemical space, depending on biomedical experts to manually design molecules is exceedingly expensive. Utilizing gener...

Interdisciplinary approach to identify language markers for post-traumatic stress disorder using machine learning and deep learning.

Scientific reports
Post-traumatic stress disorder (PTSD) lacks clear biomarkers in clinical practice. Language as a potential diagnostic biomarker for PTSD is investigated in this study. We analyze an original cohort of 148 individuals exposed to the November 13, 2015,...

Enhancement of OCTimages by unsupervised deep learning.

Physics in medicine and biology
. The quality of optical coherence tomography (OCT)images is crucial for clinical visualization of early disease. As a three dimensional and coherent imaging, defocus and speckle noise are inevitable, which seriously affect evaluation of microstructu...

Evaluation of an automated clinical decision system with deep learning dose prediction and NTCP model for prostate cancer proton therapy.

Physics in medicine and biology
To evaluate the feasibility of using a deep learning dose prediction approach to identify patients who could benefit most from proton therapy based on the normal tissue complication probability (NTCP) model.Two 3D UNets were established to predict ph...

Deep learning based linear energy transfer calculation for proton therapy.

Physics in medicine and biology
This study aims to address the limitations of traditional methods for calculating linear energy transfer (LET), a critical component in assessing relative biological effectiveness (RBE). Currently, Monte Carlo (MC) simulation, the gold-standard for a...

Generalizing the Enhanced-Deep-Super-Resolution Neural Network to Brain MR Images: A Retrospective Study on the Cam-CAN Dataset.

eNeuro
The Enhanced-Deep-Super-Resolution (EDSR) model is a state-of-the-art convolutional neural network suitable for improving image spatial resolution. It was previously trained with general-purpose pictures and then, in this work, tested on biomedical m...

Multimodal Brain Tumor Classification Using Convolutional Tumnet Architecture.

Behavioural neurology
The most common and aggressive tumor is brain malignancy, which has a short life span in the fourth grade of the disease. As a result, the medical plan may be a crucial step toward improving the well-being of a patient. Both diagnosis and therapy are...

Identification of biological indicators for human exposure toxicology in smart cities based on public health data and deep learning.

Frontiers in public health
With the acceleration of urbanization, the risk of urban population exposure to environmental pollutants is increasing. Protecting public health is the top priority in the construction of smart cities. The purpose of this study is to propose a method...

Hybrid deep learning approach for sentiment analysis using text and emojis.

Network (Bristol, England)
Sentiment Analysis (SA) is a technique for categorizing texts based on the sentimental polarity of people's opinions. This paper introduces a sentiment analysis (SA) model with text and emojis. The two preprocessed data's are data with text and emoji...