AIMC Topic: Image Processing, Computer-Assisted

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A Novel Deep Learning-Based Black Fungus Disease Identification Using Modified Hybrid Learning Methodology.

Contrast media & molecular imaging
Currently, countries across the world are suffering from a prominent viral infection called COVID-19. Most countries are still facing several issues due to this disease, which has resulted in several fatalities. The first COVID-19 wave caused devasta...

Breast lesions classifications of mammographic images using a deep convolutional neural network-based approach.

PloS one
Breast cancer is one of the worst illnesses, with a higher fatality rate among women globally. Breast cancer detection needs accurate mammography interpretation and analysis, which is challenging for radiologists owing to the intricate anatomy of the...

WBC-AMNet: Automatic classification of WBC images using deep feature fusion network based on focalized attention mechanism.

PloS one
The recognition and classification of White Blood Cell (WBC) play a remarkable role in blood-related diseases (i.e., leukemia, infections) diagnosis. For the highly similar morphology of different WBC subtypes, it is too confused to classify the WBC ...

Deep Learning Algorithm for Automated Detection of Polycystic Ovary Syndrome Using Scleral Images.

Frontiers in endocrinology
The high prevalence of polycystic ovary syndrome (PCOS) among reproductive-aged women has attracted more and more attention. As a common disorder that is likely to threaten women's health physically and mentally, the detection of PCOS is a growing pu...

Jointly estimating parametric maps of multiple diffusion models from undersampled q-space data: A comparison of three deep learning approaches.

Magnetic resonance in medicine
PURPOSE: While advanced diffusion techniques have been found valuable in many studies, their clinical availability has been hampered partly due to their long scan times. Moreover, each diffusion technique can only extract a few relevant microstructur...

Automatic deep learning multicontrast corpus callosum segmentation in multiple sclerosis.

Journal of neuroimaging : official journal of the American Society of Neuroimaging
BACKGROUND AND PURPOSE: Corpus callosum (CC) atrophy is predictive of future disability in multiple sclerosis (MS). However, current segmentation methods are either labor- or computationally intensive. We therefore developed an automated deep learnin...

Volumetric segmentation of white matter tracts with label embedding.

NeuroImage
Convolutional neural networks have achieved state-of-the-art performance for white matterĀ (WM) tract segmentation based on diffusion magnetic resonance imaging (dMRI). However, the segmentation can still be difficult for challenging WM tracts with th...

Testing Precision Limits of Neural Network-Based Quality Control Metrics in High-Throughput Digital Microscopy.

Pharmaceutical research
OBJECTIVE: Digital microscopy is used to monitor particulates such as protein aggregates within biopharmaceutical products. The images that result encode a wealth of information that is underutilized in pharmaceutical process monitoring. For example,...

Attention-guided deep learning for gestational age prediction using fetal brain MRI.

Scientific reports
Magnetic resonance imaging offers unrivaled visualization of the fetal brain, forming the basis for establishing age-specific morphologic milestones. However, gauging age-appropriate neural development remains a difficult task due to the constantly c...

A feasible method to evaluate deformable image registration with deep learning-based segmentation.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: We have developed a feasible method to evaluate deformable image registration using deep learning (DL)-based segmentation.