AIMC Topic: Image Interpretation, Computer-Assisted

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

Computers in biology and medicine Jan 29, 2025
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...
Neural Networks, Computer Female Breast Neoplasms Humans Deep Learning Image Processing, Computer-Assisted Early Detection of Cancer Image Interpretation, Computer-Assisted
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Dilated SE-DenseNet for brain tumor MRI classification.

Scientific reports Jan 28, 2025
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...
Machine Learning Algorithms Brain Neoplasms Magnetic Resonance Imaging Convolutional Neural Networks Humans Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Neural Networks, Computer
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Identification of diabetic retinopathy lesions in fundus images by integrating CNN and vision mamba models.

PloS one Jan 28, 2025
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...
Fundus Oculi Algorithms Humans Image Interpretation, Computer-Assisted Deep Learning Diabetic Retinopathy Image Processing, Computer-Assisted Neural Networks, Computer
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Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions.

IEEE reviews in biomedical engineering Jan 28, 2025
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...
Magnetic Resonance Imaging Mammography Breast Image Interpretation, Computer-Assisted Female Humans Breast Neoplasms Deep Learning
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Identifying Primary Sites of Spinal Metastases: Expert-Derived Features vs. ResNet50 Model Using Nonenhanced MRI.

Journal of magnetic resonance imaging : JMRI Jan 27, 2025
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.
Aged Adult Male Retrospective Studies Female Reproducibility of Results Magnetic Resonance Imaging Middle Aged Humans Spine Deep Learning Image Interpretation, Computer-Assisted Artificial Intelligence ROC Curve Spinal Neoplasms
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Integration of MRI radiomics and clinical data for preoperative prediction of vascular invasion in breast cancer: A deep learning approach.

Magnetic resonance imaging Jan 27, 2025
BACKGROUND: Accurate preoperative prediction of vascular invasion in breast cancer is crucial for surgical planning and patient management. MRI radiomics has shown promise in enhancing diagnostic precision. This study aims to evaluate the effectivene...
Middle Aged Deep Learning Image Processing, Computer-Assisted Contrast Media Magnetic Resonance Imaging Neoplasm Invasiveness Sensitivity and Specificity Breast Neoplasms Radiomics Female Aged Adult Humans Retrospective Studies Diffusion Magnetic Resonance Imaging Breast Image Interpretation, Computer-Assisted Preoperative Care
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Entity-level multiple instance learning for mesoscopic histopathology images classification with Bayesian collaborative learning and pathological prior transfer.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Jan 27, 2025
BACKGROUND: Entity-level pathologic structures with independent structures and functions are at a mesoscopic scale between the cell-level and slide-level, containing limited structures thus providing fewer instances for multiple instance learning. Th...
Humans Bayes Theorem Machine Learning Image Interpretation, Computer-Assisted Algorithms Multiple-Instance Learning Algorithms
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SeLa-MIL: Developing an instance-level classifier via weakly-supervised self-training for whole slide image classification.

Computer methods and programs in biomedicine Jan 27, 2025
BACKGROUND AND OBJECTIVE: Pathology image classification is crucial in clinical cancer diagnosis and computer-aided diagnosis. Whole Slide Image (WSI) classification is often framed as a multiple instance learning (MIL) problem due to the high cost o...
Supervised Machine Learning Humans Image Interpretation, Computer-Assisted Neoplasms Machine Learning Algorithms Diagnosis, Computer-Assisted Image Processing, Computer-Assisted
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LiteMamba-Bound: A lightweight Mamba-based model with boundary-aware and normalized active contour loss for skin lesion segmentation.

Methods (San Diego, Calif.) Jan 27, 2025
In the field of medical science, skin segmentation has gained significant importance, particularly in dermatology and skin cancer research. This domain demands high precision in distinguishing critical regions (such as lesions or moles) from healthy ...
Image Interpretation, Computer-Assisted Deep Learning Image Processing, Computer-Assisted Skin Humans Neural Networks, Computer Algorithms Skin Neoplasms
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Enhanced brain tumor detection and segmentation using densely connected convolutional networks with stacking ensemble learning.

Computers in biology and medicine Jan 24, 2025
- Brain tumors (BT), both benign and malignant, pose a substantial impact on human health and need precise and early detection for successful treatment. Analysing magnetic resonance imaging (MRI) image is a common method for BT diagnosis and segmenta...
Humans Neural Networks, Computer Convolutional Neural Networks Image Processing, Computer-Assisted Magnetic Resonance Imaging Machine Learning Brain Neoplasms Deep Learning Image Interpretation, Computer-Assisted Ensemble Learning
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