Brain tumors are an increasing global health concern, and accurate diagnosis is essential for improving patient outcomes. Although existing Magnetic Resonance Imaging (MRI)-based machine learning utilizes computer vision for tumor diagnosis, these me...
BACKGROUND: To develop and validate a deep learning tool for the automatic segmentation of pancreatic solid neoplasms and to establish a radiomics model for diagnosing these solid neoplasms in MRI.
Diabetic retinopathy (DR), a serious eye condition in diabetic patients, requires early and precise detection for effective treatment. Late diagnosis and poor blood sugar control exacerbate this condition, highlighting the need for improved diagnosti...
Developments in image captioning technologies played a crucial role in improving the quality of life for individuals with visual impairments, advancing better social inclusivity. Image captioning is the task of representing the visual content of the ...
Understanding bone remodeling and disease progression is crucial in preclinical skeletal research, particularly for assessing pharmacological and mechanical interventions in the long bones of murine models. High-resolution micro-computed tomography (...
Segmentation and detection of biological objects in fluorescence microscopy is of paramount importance in cell imaging. Deep learning approaches have recently shown promise to advance, automatize and accelerate analysis. However, most of the interest...
Full-field digital mammography (FFDM) is the most common imaging technique for breast cancer screening programs. Still, it is limited by noise from quantum effects, electronic issues, and X-ray scattering, affecting the image quality. Traditional den...
Age-related macular degeneration (AMD) is a common cause of vision loss in older adults. The automated grading of AMD from fundus images can aid in early detection and treatment. In this research, we propose a comprehensive framework that can enhance...
Machine learning studies for Raman imaging have addressed the differentiability of normal and diseased states in biomedical applications by grouping a set of Raman spectra in terms of spectral similarity over the sample. However, Raman imaging provid...
Medical image segmentation is a crucial technology for disease diagnosis and treatment planning. However, current approaches face challenges in capturing global semantic dependencies and integrating cross-layer features. While Convolutional Neural Ne...
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