AIMC Topic: Image Processing, Computer-Assisted

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Explicit Abnormality Extraction for Unsupervised Motion Artifact Reduction in Magnetic Resonance Imaging.

IEEE journal of biomedical and health informatics
Motion artifacts compromise the quality of magnetic resonance imaging (MRI) and pose challenges to achieving diagnostic outcomes and image-guided therapies. In recent years, supervised deep learning approaches have emerged as successful solutions for...

Histopathological image based breast cancer diagnosis using deep learning and bio inspired optimization.

Scientific reports
Breast cancer diagnosis remains a crucial challenge in medical research, necessitating accurate and automated detection methods. This study introduces an advanced deep learning framework for histopathological image classification, integrating AlexNet...

Deep learning-driven automated mitochondrial segmentation for analysis of complex transmission electron microscopy images.

Scientific reports
Mitochondria are central to cellular energy production and regulation, with their morphology tightly linked to functional performance. Precise analysis of mitochondrial ultrastructure is crucial for understanding cellular bioenergetics and pathology....

Deep learning based motion correction in ultrasound microvessel imaging approach improves thyroid nodule classification.

Scientific reports
To address inter-frame motion artifacts in ultrasound quantitative high-definition microvasculature imaging (qHDMI), we introduced a novel deep learning-based motion correction technique. This approach enables the derivation of more accurate quantita...

An efficient plant disease detection using transfer learning approach.

Scientific reports
Plant diseases pose significant challenges to farmers and the agricultural sector at large. However, early detection of plant diseases is crucial to mitigating their effects and preventing widespread damage, as outbreaks can severely impact the produ...

Bayesian deep-learning structured illumination microscopy enables reliable super-resolution imaging with uncertainty quantification.

Nature communications
The objective of optical super-resolution imaging is to acquire reliable sub-diffraction information on bioprocesses to facilitate scientific discovery. Structured illumination microscopy (SIM) is acknowledged as the optimal modality for live-cell su...

Automated classification of midpalatal suture maturation stages from CBCTs using an end-to-end deep learning framework.

Scientific reports
Accurate classification of midpalatal suture maturation stages is critical for orthodontic diagnosis, treatment planning, and the assessment of maxillary growth. Cone Beam Computed Tomography (CBCT) imaging offers detailed insights into this craniofa...

Deep learning reconstruction enhances tophus detection in a dual-energy CT phantom study.

Scientific reports
This study aimed to compare two deep learning reconstruction (DLR) techniques (AiCE mild; AiCE strong) with two established methods-iterative reconstruction (IR) and filtered back projection (FBP)-for the detection of monosodium urate (MSU) in dual-e...

DCA-U-Net: a deep learning network for segmentation of laser-induced thermal damage regions in mouse skin OCT images.

Biomedical physics & engineering express
Laser-induced thermal injury is a common form of skin damage in clinical treatment, and accurately assessing the extent of injury and treatment efficacy is crucial for patient recovery. In recent years, deep learning models have been increasingly app...

Near-zero photon bioimaging by fusing deep learning and ultralow-light microscopy.

Proceedings of the National Academy of Sciences of the United States of America
Enhancing the reliability and reproducibility of optical microscopy by reducing specimen irradiance continues to be an important biotechnology target. As irradiance levels are reduced, however, the particle nature of light is heightened, giving rise ...