AIMC Topic: Image Processing, Computer-Assisted

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Transfer learning based deep architecture for lung cancer classification using CT image with pattern and entropy based feature set.

Scientific reports
Early detection of lung cancer, which remains one of the leading causes of death worldwide, is important for improved prognosis, and CT scanning is an important diagnostic modality. Lung cancer classification according to CT scan is challenging since...

Unsupervised learning for labeling global glomerulosclerosis.

Computers in biology and medicine
BACKGROUND: Labeling images for supervised learning in nephropathology is highly time-consuming and dependent on domain-expertise. Unsupervised strategies have been suggested for mitigating this bottleneck. For instance, previous work suggested that ...

Fluid-SegNet: Multi-dimensional loss-driven Y-Net with dilated convolutions for OCT B-scan fluid segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Optical Coherence Tomography (OCT) is a widely utilized imaging modality in clinical ophthalmology, particularly for retinal imaging. B-scan is a two-dimensional slice of the OCT volume. It enables high-resolution cross-sectional visualization of ret...

An explainable vision transformer with transfer learning based efficient drought stress identification.

Plant molecular biology
Early detection of drought stress is critical for taking timely measures for reducing crop loss before the drought impact becomes irreversible. The subtle phenotypical and physiological changes in response to drought stress are captured by non-invasi...

A successive framework for brain tumor interpretation using Yolo variants.

Scientific reports
Accurate identification and segmentation of brain tumors in Magnetic Resonance Imaging (MRI) images are critical for timely diagnosis and treatment. MRI is frequently used to diagnose these disorders; however medical professionals find it challenging...

Development of a novel deep learning method that transforms tabular input variables into images for the prediction of SLD.

Scientific reports
Steatotic liver disease (SLD), formerly named fatty liver disease, has a prevalence estimated at 30-38% in adults. Detection of SLD is important, since prompt initiation of treatment can stop disease progression, lead to a reduction in adverse outcom...

Deep learning-based real-time detection of head and neck tumors during radiation therapy.

Physics in medicine and biology
Clinical drivers for real-time head and neck (H&N) tumor tracking during radiation therapy (RT) are accounting for motion caused by changes to the immobilization mask fit, and to reduce mask-related patient distress by replacing the masks with patien...

Efficacy of image similarity as a metric for augmenting small dataset retinal image segmentation.

Computers in biology and medicine
Synthetic images are an option for augmenting limited medical imaging datasets to improve the performance of various machine learning models. A common metric for evaluating synthetic image quality is the Fréchet Inception Distance (FID) which measure...

M3-Net++: A multi-scale, multi-level, multi-stream network for nuclei segmentation in breast cancer histopathology using hierarchical context extraction and hybrid loss optimization.

Computers in biology and medicine
Breast cancer remains a leading cause of morbidity and mortality worldwide. Histopathology, particularly the analysis of nuclear morphology in tissue samples, is critical for diagnosing and understanding the progression of breast cancer. Accurate nuc...

Leveraging weak supervision for cell localization in digital pathology using multitask learning and consistency loss.

Computers in biology and medicine
Cell detection and segmentation are integral parts of automated systems in digital pathology. Encoder-decoder networks have emerged as a promising solution for these tasks. However, training of these networks has typically required full boundary anno...