AIMC Topic: Image Interpretation, Computer-Assisted

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Developing an interpretable machine learning model for diagnosing gout using clinical and ultrasound features.

European journal of radiology
OBJECTIVE: To develop a machine learning (ML) model using clinical data and ultrasound features for gout prediction, and apply SHapley Additive exPlanations (SHAP) for model interpretation.

Towards unbiased skin cancer classification using deep feature fusion.

BMC medical informatics and decision making
This paper introduces SkinWiseNet (SWNet), a deep convolutional neural network designed for the detection and automatic classification of potentially malignant skin cancer conditions. SWNet optimizes feature extraction through multiple pathways, emph...

BCT-Net: semantic-guided breast cancer segmentation on BUS.

Medical & biological engineering & computing
Accurately and swiftly segmenting breast tumors is significant for cancer diagnosis and treatment. Ultrasound imaging stands as one of the widely employed methods in clinical practice. However, due to challenges such as low contrast, blurred boundari...

A deep learning based model for diabetic retinopathy grading.

Scientific reports
Diabetic retinopathy stands as a leading cause of blindness among people. Manual examination of DR images is labor-intensive and prone to error. Existing methods to detect this disease often rely on handcrafted features which limit the adaptability a...

Improved early detection accuracy for breast cancer using a deep learning framework in medical imaging.

Computers in biology and medicine
PROBLEM: The most prevalent cancer in women is breast cancer (BC), and effective treatment depends on being detected early. Many people seek medical imaging techniques to help in the early detection of problems, but results often need to be corrected...

Dilated SE-DenseNet for brain tumor MRI classification.

Scientific reports
In the field of medical imaging, particularly MRI-based brain tumor classification, we propose an advanced convolutional neural network (CNN) leveraging the DenseNet-121 architecture, enhanced with dilated convolutional layers and Squeeze-and-Excitat...

Identification of diabetic retinopathy lesions in fundus images by integrating CNN and vision mamba models.

PloS one
Diabetic retinopathy, a retinal disorder resulting from diabetes mellitus, is a prominent cause of visual degradation and loss among the global population. Therefore, the identification and classification of diabetic retinopathy are of utmost importa...

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions.

IEEE reviews in biomedical engineering
Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep l...

Identifying Primary Sites of Spinal Metastases: Expert-Derived Features vs. ResNet50 Model Using Nonenhanced MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: The spinal column is a frequent site for metastases, affecting over 30% of solid tumor patients. Identifying the primary tumor is essential for guiding clinical decisions but often requires resource-intensive diagnostics.