AIMC Topic: Image Interpretation, Computer-Assisted

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Deep learning quantifies pathologists' visual patterns for whole slide image diagnosis.

Nature communications
Based on the expertise of pathologists, the pixelwise manual annotation has provided substantial support for training deep learning models of whole slide images (WSI)-assisted diagnostic. However, the collection of pixelwise annotation demands massiv...

Hybrid transfer learning and self-attention framework for robust MRI-based brain tumor classification.

Scientific reports
Brain tumors are a significant contributor to cancer-related deaths worldwide. Accurate and prompt detection is crucial to reduce mortality rates and improve patient survival prospects. Magnetic Resonance Imaging (MRI) is crucial for diagnosis, but m...

Generative AI for weakly supervised segmentation and downstream classification of brain tumors on MR images.

Scientific reports
Segmenting abnormalities is a leading problem in medical imaging. Using machine learning for segmentation generally requires manually annotated segmentations, demanding extensive time and resources from radiologists. We propose a weakly supervised ap...

Automatic melanoma detection using an optimized five-stream convolutional neural network.

Scientific reports
Melanoma is among the deadliest forms of malignant skin cancer, with the number of cases increasing dramatically worldwide. Its early and accurate diagnosis is crucial for effective treatment. However, automatic melanoma detection has several signifi...

Enhancing weakly supervised data augmentation networks for thyroid nodule assessment using traditional and doppler ultrasound images.

Computers in biology and medicine
Thyroid ultrasound (US) is an essential tool for detecting and characterizing thyroid nodules. In this study, we propose an innovative approach to enhance thyroid nodule assessment by integrating Doppler US images with grayscale US images through wea...

U-Net-based architecture with attention mechanisms and Bayesian Optimization for brain tumor segmentation using MR images.

Computers in biology and medicine
As technological innovation in computers has advanced, radiologists may now diagnose brain tumors (BT) with the use of artificial intelligence (AI). In the medical field, early disease identification enables further therapies, where the use of AI sys...

Explainable classification of Parkinson's disease with different motor subtypes by analyzing the synthetic MRI quantitative parameters of subcortical nuclei.

European journal of radiology
OBJECTIVES: To explore differences in quantitative parameters of subcortical nuclei using synthetic MRI across different motor subtypes of Parkinson's Disease (PD), and to develop an interpretable model for distinguishing PD subtypes.

FeaCL: Carotid plaque classification from ultrasound images using feature-level and instance-level contrast learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The classification of carotid plaques from ultrasound images in clinical application is crucial for predicting patient risks of cardiovascular and cerebrovascular diseases, as well as for developing appropriate treatment strategies. Although the effe...

A multimodal skin lesion classification through cross-attention fusion and collaborative edge computing.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Skin cancer is a significant global health concern requiring early and accurate diagnosis to improve patient outcomes. While deep learning-based computer-aided diagnosis (CAD) systems have emerged as effective diagnostic support tools, they often fac...

Detection of breast cancer using fractional discrete sinc transform based on empirical Fourier decomposition.

Bio-medical materials and engineering
Breast cancer is the most common cause of death among women worldwide. Early detection of breast cancer is important; for saving patients' lives. Ultrasound and mammography are the most common noninvasive methods for detecting breast cancer. Computer...