AIMC Topic: Deep Learning

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MOB-CBAM: A dual-channel attention-based deep learning generalizable model for breast cancer molecular subtypes prediction using mammograms.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Deep Learning models have emerged as a significant tool in generating efficient solutions for complex problems including cancer detection, as they can analyze large amounts of data with high efficiency and performance. Recen...

Novel 3D-based deep learning for classification of acute exacerbation of idiopathic pulmonary fibrosis using high-resolution CT.

BMJ open respiratory research
PURPOSE: Acute exacerbation of idiopathic pulmonary fibrosis (AE-IPF) is the primary cause of death in patients with IPF, characterised by diffuse, bilateral ground-glass opacification on high-resolution CT (HRCT). This study proposes a three-dimensi...

A robust deep learning model for the classification of dental implant brands.

Journal of stomatology, oral and maxillofacial surgery
OBJECTIVE: In cases where the brands of implants are not known, treatment options can be significantly limited in potential complications arising from implant procedures. This research aims to explore the application of deep learning techniques for t...

Automated Spontaneous Echo Contrast Detection Using a Multisequence Attention Convolutional Neural Network.

Ultrasound in medicine & biology
OBJECTIVE: Spontaneous echo contrast (SEC) is a vascular ultrasound finding associated with increased thromboembolism risk. However, identification requires expert determination and clinician time to report. We developed a deep learning model that ca...

GraphsformerCPI: Graph Transformer for Compound-Protein Interaction Prediction.

Interdisciplinary sciences, computational life sciences
Accurately predicting compound-protein interactions (CPI) is a critical task in computer-aided drug design. In recent years, the exponential growth of compound activity and biomedical data has highlighted the need for efficient and interpretable pred...

IODeep: An IOD for the introduction of deep learning in the DICOM standard.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In recent years, Artificial Intelligence (AI) and in particular Deep Neural Networks (DNN) became a relevant research topic in biomedical image segmentation due to the availability of more and more data sets along with the e...

DeePNAP: A Deep Learning Method to Predict Protein-Nucleic Acid Binding Affinity from Their Sequences.

Journal of chemical information and modeling
Predicting the protein-nucleic acid (PNA) binding affinity solely from their sequences is of paramount importance for the experimental design and analysis of PNA interactions (PNAIs). A large number of currently developed models for binding affinity ...

"sCT-Feasibility" - a feasibility study for deep learning-based MRI-only brain radiotherapy.

Radiation oncology (London, England)
BACKGROUND: Radiotherapy (RT) is an important treatment modality for patients with brain malignancies. Traditionally, computed tomography (CT) images are used for RT treatment planning whereas magnetic resonance imaging (MRI) images are used for tumo...

Enhancing parasitic organism detection in microscopy images through deep learning and fine-tuned optimizer.

Scientific reports
Parasitic organisms pose a major global health threat, mainly in regions that lack advanced medical facilities. Early and accurate detection of parasitic organisms is vital to saving lives. Deep learning models have uplifted the medical sector by pro...