AIMC Topic: Deep Learning

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MP-FocalUNet: Multiscale parallel focal self-attention U-Net for medical image segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Medical image segmentation has been significantly improved in recent years with the progress of Convolutional Neural Networks (CNNs). Due to the inherent limitations of convolutional operations, CNNs perform poorly in learni...

A systematic review on the impact of artificial intelligence on electrocardiograms in cardiology.

International journal of medical informatics
BACKGROUND: Artificial intelligence (AI) has revolutionized numerous industries, enhancing efficiency, scalability, and insight generation. In cardiology, particularly through electrocardiogram (ECG) analysis, AI has the potential to improve diagnost...

A dual-decoder banded convolutional attention network for bone segmentation in ultrasound images.

Medical physics
BACKGROUND: Ultrasound (US) has great potential for application in computer-assisted orthopedic surgery (CAOS) due to its non-radiative, cost-effective, and portable traits. However, bone segmentation from low-quality US images has been challenging. ...

Prediction of Visual Acuity After Cataract Surgery by Deep Learning Methods Using Clinical Information and Color Fundus Photography.

Current eye research
PURPOSE: To examine the performance of deep-learning models that predicts the visual acuity after cataract surgery using preoperative clinical information and color fundus photography (CFP).

Hierarchical Graph Attention Network with Positive and Negative Attentions for Improved Interpretability: ISA-PN.

Journal of chemical information and modeling
With the advancement of deep learning (DL) methods in chemistry and materials science, the interpretability of DL models has become a critical issue in elucidating quantitative (molecular) structure-property relationships. Although attention mechanis...

Domain generalization for mammographic image analysis with contrastive learning.

Computers in biology and medicine
The deep learning technique has been shown to be effectively addressed several image analysis tasks in the computer-aided diagnosis scheme for mammography. The training of an efficacious deep learning model requires large data with diverse styles and...

Natural compounds for Alzheimer's prevention and treatment: Integrating SELFormer-based computational screening with experimental validation.

Computers in biology and medicine
BACKGROUND: This study aimed to develop and apply a novel computational pipeline combining SELFormer, a transformer architecture-based chemical language model, with advanced deep learning techniques to predict natural compounds (NCs) with potential i...

Classification of speech arrests and speech impairments during awake craniotomy: a multi-databases analysis.

International journal of computer assisted radiology and surgery
PURPOSE: Awake craniotomy presents a unique opportunity to map and preserve critical brain functions, particularly speech, during tumor resection. The ability to accurately assess linguistic functions in real-time not only enhances surgical precision...

Enhancing brain tumor classification by integrating radiomics and deep learning features: A comprehensive study utilizing ensemble methods on MRI scans.

Journal of X-ray science and technology
BACKGROUND AND OBJECTIVE: This study aims to assess the effectiveness of combining radiomics features (RFs) with deep learning features (DFs) for classifying brain tumors-specifically Glioma, Meningioma, and Pituitary Tumor-using MRI scans and advanc...

Deep learning for cardiac imaging: focus on myocardial diseases, a narrative review.

Hellenic journal of cardiology : HJC = Hellenike kardiologike epitheorese
The integration of computational technologies into cardiology has significantly advanced the diagnosis and management of cardiovascular diseases. Computational cardiology, particularly, through cardiovascular imaging and informatics, enables a precis...