Medical image analysis has significantly benefited from advancements in deep learning, particularly in the application of Generative Adversarial Networks (GANs) for generating realistic and diverse images that can augment training datasets. The commo...
RATIONALE AND OBJECTIVES: Magnetic resonance imaging (MRI) is a vital tool for diagnosing neurological disorders, frequently utilising gadolinium-based contrast agents (GBCAs) to enhance resolution and specificity. However, GBCAs present certain risk...
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
The virus poses a longstanding and enduring danger to various forms of life. Despite the ongoing endeavors to combat viral diseases, there exists a necessity to explore and develop novel therapeutic options. Antiviral peptides are bioactive molecules...
Neural networks : the official journal of the International Neural Network Society
Dec 5, 2024
The spike-based neuronal responses within the ventral intraparietal area (VIP) exhibit intricate spatial and temporal dynamics in the posterior parietal cortex, presenting decoding challenges such as limited data availability at the biological popula...
Deep learning-based generative adversarial network (GAN) frameworks have recently been developed to expedite the drug discovery process. These models generate novel molecules from scratch and validate them through molecular docking simulation to iden...
Journal of gastroenterology and hepatology
Nov 14, 2024
BACKGROUND AND AIM: Computed tomography of the abdomen exhibits subtle and complex features of liver lesions, subjectively interpreted by physicians. We developed a deep learning-based localization and classification (DLLC) system for focal liver les...
The Fontan procedure is the definitive palliation for pediatric patients born with single ventricles. Surgical planning for the Fontan procedure has emerged as a promising vehicle toward optimizing outcomes, where pre-operative measurements are used ...
The PhysioNet/Computing in Cardiology (CinC) Challenge 2016 dataset has driven significant advancements in automated heart sound analysis using machine learning (ML) and deep learning (DL). However, these efforts are constrained by the dataset's limi...
Arrhythmia classifiers relying on supervised deep learning models usually require a substantial amount of labeled clinical data. The distribution of these labels is strictly related to the statistics of cardiovascular diseases among the population, w...
Raman imaging utilizes molecular fingerprint information to visualize the spatial distribution of a substance within the scanned area. Subject to its scanning mechanism, it usually costs a prolonged data acquisition duration for achieving high-resolu...
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