In recent years, scientists have been trying to convert magnetic resonance imaging (MRI) images into computed tomography (CT) images for dose calculations while taking advantage of the benefits of MRI images. The main approaches for image conversion ...
The detection, segmentation, and differentiation of benign and malignant nuclei from the histopathology images is a challenging task for the early diagnosis of breast cancer. Misinterpretation of True Negative (TN) and False Positive (FP) can generat...
In computational pathology, extracting and representing spatial features from gigapixel whole slide images (WSIs) are fundamental tasks, but due to their large size, WSIs are typically segmented into smaller tiles. A critical aspect of analyzing WSIs...
Accurate and interpretable age estimation and gender classification are essential in forensic and clinical diagnostics, particularly when using high-dimensional medical imaging data such as Cone Beam Computed Tomography (CBCT). Traditional CBCT-based...
Recent advances in deep learning-based image reconstruction techniques have led to significant progress in phase retrieval using digital in-line holographic microscopy (DIHM). However, existing phase retrieval methods have technical limitations in 3D...
Arthropods have intricate compound eyes and optic neuropils, exhibiting exceptional visual capabilities. Combining the strengths of digital imaging with the features of natural arthropod visual systems offers a promising approach to harness wide-angl...
Deep learning models are increasingly being implemented for automated medical image analysis to inform patient care. Most models, however, lack uncertainty information, without which the reliability of model outputs cannot be ensured. Several uncerta...
Current brain tumor segmentation methods often utilize a U-Net architecture based on efficient convolutional neural networks. While effective, these architectures primarily model local dependencies, lacking the ability to capture global interactions ...
This article introduces a novel deep-learning based framework, Super-resolution/Denoising network (SDNet), for simultaneous denoising and super-resolution of swept-source optical coherence tomography (SS-OCT) images. The novelty of this work lies in ...
. Positron emission tomography with 2-deoxy-2-[fluorine-18]fluoro-D-glucose integrated with computed tomography (18F-FDG PET-CT) is a multi-modality medical imaging technique widely used for screening and diagnosis of lesions and tumors, in which, CT...
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