AIMC Topic: Deep Learning

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A multi-class fundus disease classification system based on an adaptive scale discriminator and hybrid loss.

Computational biology and chemistry
Fundus images are crucial in the observation and detection of ophthalmic diseases. However, detecting multiple ophthalmic diseases from fundus images using deep learning techniques is a complex and challenging task One challenge is the complexity of ...

AI driven interpretable deep learning based fetal health classification.

SLAS technology
In this study, a deep learning model is proposed for the classification of fetal health into 3 categories: Normal, suspect, and pathological. The primary objective is to utilize the power of deep learning to improve the efficiency and effectiveness o...

Segmentation of four-chamber view images in fetal ultrasound exams using a novel deep learning model ensemble method.

Computers in biology and medicine
Fetal echocardiography, a specialized ultrasound application commonly utilized for fetal heart assessment, can greatly benefit from automated segmentation of anatomical structures, aiding operators in their evaluations. We introduce a novel approach ...

Sex dimorphism and hormesis response to polystyrene microplastic exposure in kinematics and metabolism of Drosophila model based on deep learning.

Journal of environmental management
The emergence of microplastics (MPs) has become a significant focus of environmental pollution, prompting widespread concern regarding its potential toxicity and impact on the environment and organisms. Recent research indicates notable alterations i...

Signal processing for enhancing railway communication by integrating deep learning and adaptive equalization techniques.

PloS one
With the increasing amount of data in railway communication system, the conventional wireless high-frequency communication technology cannot meet the requirements of modern communication and needs to be improved. In order to meet the requirements of ...

Application of a Deep Learning-Based Contrast-Boosting Algorithm to Low-Dose Computed Tomography Pulmonary Angiography With Reduced Iodine Load.

Journal of computer assisted tomography
OBJECTIVE: The aim of this study was to assess the effectiveness of a deep learning-based image contrast-boosting algorithm by enhancing the image quality of low-dose computed tomography pulmonary angiography at reduced iodine load.

Automated tumor localization and segmentation through hybrid neural network in head and neck cancer.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists
PURPOSE: Head and Neck (H&N) cancer accounts for 3% of cancer cases in the United States. Precise tumor segmentation in H&N is of utmost importance for treatment planning and administering personalized treatment dose. We aimed to develop an automatic...

An anthropomorphic diagnosis system of pulmonary nodules using weak annotation-based deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The accurate categorization of lung nodules in CT scans is an essential aspect in the prompt detection and diagnosis of lung cancer. The categorization of grade and texture for nodules is particularly significant since it can aid radiologists and cli...

Assessing the impact of ultrasound image standardization in deep learning-based segmentation of carotid plaque types.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Carotid B-mode ultrasound (CBUS) imaging is often used to detect and assess atherosclerotic plaques. Doctors often need to segment plaques in the CBUS images to further examine them. Multiple studies have proposed two-dimens...

Evaluating retinal blood vessels for predicting white matter hyperintensities in ischemic stroke: A deep learning approach.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVE: This study aims to investigate whether a deep learning approach incorporating retinal blood vessels can effectively identify ischemic stroke patients with a high burden of White Matter Hyperintensities (WMH) using Nuclear Magnetic Resonanc...