Deep learning techniques for liver and liver tumor segmentation: A review
Liver and liver tumor segmentation from 3D volumetric images has been an active research
area in the medical image processing domain for the last few decades. The existence of …
area in the medical image processing domain for the last few decades. The existence of …
Deep learning-based artificial intelligence applications in prostate MRI: brief summary
Prostate cancer (PCa) is the most common cancer type in males in the Western World. MRI
has an established role in diagnosis of PCa through guiding biopsies. Due to multistep …
has an established role in diagnosis of PCa through guiding biopsies. Due to multistep …
Mitigating bias in radiology machine learning: 1. Data handling
Minimizing bias is critical to adoption and implementation of machine learning (ML) in
clinical practice. Systematic mathematical biases produce consistent and reproducible …
clinical practice. Systematic mathematical biases produce consistent and reproducible …
An EfficientNet-based modified sigmoid transform for enhancing dermatological macro-images of melanoma and nevi skin lesions
Background and objective: During the initial stages, skin lesions may not have sufficient
intensity difference or contrast from the background region on dermatological macro-images …
intensity difference or contrast from the background region on dermatological macro-images …
[HTML][HTML] Generating 3D TOF-MRA volumes and segmentation labels using generative adversarial networks
Deep learning requires large labeled datasets that are difficult to gather in medical imaging
due to data privacy issues and time-consuming manual labeling. Generative Adversarial …
due to data privacy issues and time-consuming manual labeling. Generative Adversarial …
Detection of retinopathy disease using morphological gradient and segmentation approaches in fundus images
M Toğaçar - Computer Methods and Programs in Biomedicine, 2022 - Elsevier
Background and objective Diabetes-related cases can cause glaucoma, cataracts, optic
neuritis, paralysis of the eye muscles, or various retinal damages over time. Diabetic …
neuritis, paralysis of the eye muscles, or various retinal damages over time. Diabetic …
Training auxiliary prototypical classifiers for explainable anomaly detection in medical image segmentation
Abstract Machine learning-based algorithms using fully convolutional networks (FCNs) have
been a promising option for medical image segmentation. However, such deep networks …
been a promising option for medical image segmentation. However, such deep networks …
GROUSE: A task and model agnostic wavelet-driven framework for medical imaging
In recent years, deep learning has permeated the field of medical image analysis gaining
increasing attention from clinicians. However, medical images always require specific …
increasing attention from clinicians. However, medical images always require specific …
Robust deep learning-based fault detection of planetary gearbox using enhanced health data map under domain shift problem
The conventional deep learning-based fault diagnosis approach faces challenges under the
domain shift problem, where the model encounters different working conditions from the …
domain shift problem, where the model encounters different working conditions from the …
Role of imaging and ai in the evaluation of covid-19 infection: a comprehensive survey
Abstract Coronavirus disease 2019 (COVID-19) is a respiratory illness that started and
rapidly became the pandemic of the century, as the number of people infected with it …
rapidly became the pandemic of the century, as the number of people infected with it …