AIMC Topic: Image Processing, Computer-Assisted

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Automated spectral decomposition and reconstruction of optical properties using a mixed autoencoder approach.

Journal of biomedical optics
SIGNIFICANCE: Investigating optical properties (OPs) is crucial in the field of biophotonics, as it has a broad impact on understanding light-tissue interactions. However, current techniques, such as inverse Monte Carlo simulations (IMCS), have limit...

Construction and validation of a deep learning-based diagnostic model for segmentation and classification of diabetic foot.

Frontiers in endocrinology
OBJECTIVE: This study aims to conduct an in-depth analysis of diabetic foot ulcer (DFU) images using deep learning models, achieving automated segmentation and classification of the wounds, with the goal of exploring the application of artificial int...

Deep Learning-Based Medical Ultrasound Image and Video Segmentation Methods: Overview, Frontiers, and Challenges.

Sensors (Basel, Switzerland)
The intricate imaging structures, artifacts, and noise present in ultrasound images and videos pose significant challenges for accurate segmentation. Deep learning has recently emerged as a prominent field, playing a crucial role in medical image pro...

A Multitask CNN for Near-Infrared Probe: Enhanced Real-Time Breast Cancer Imaging.

Sensors (Basel, Switzerland)
The early detection of breast cancer, particularly in dense breast tissues, faces significant challenges with traditional imaging techniques such as mammography. This study utilizes a Near-infrared Scan (NIRscan) probe and an advanced convolutional n...

Automatic identification of hard and soft tissue landmarks in cone-beam computed tomography via deep learning with diversity datasets: a methodological study.

BMC oral health
BACKGROUND: Manual landmark detection in cone beam computed tomography (CBCT) for evaluating craniofacial structures relies on medical expertise and is time-consuming. This study aimed to apply a new deep learning method to predict and locate soft an...

Multi-scale conv-attention U-Net for medical image segmentation.

Scientific reports
U-Net-based network structures are widely used in medical image segmentation. However, effectively capturing multi-scale features and spatial context information of complex organizational structures remains a challenge. To address this, we propose a ...

Phantom-based evaluation of image quality in Transformer-enhanced 2048-matrix CT imaging at low and ultralow doses.

Japanese journal of radiology
PURPOSE: To compare the quality of standard 512-matrix, standard 1024-matrix, and Swin2SR-based 2048-matrix phantom images under different scanning protocols.

Enhancing polyp classification: A comparative analysis of spatio-temporal techniques.

Medical engineering & physics
Colorectal cancer (CRC) is a major health concern, ranking as the third deadliest cancer globally. Early diagnosis of adenomatous polyps which are pre-cancerous abnormal tissue growth, is crucial for preventing CRC. Artificial intelligence-assisted n...

Novel hybrid transfer neural network for wheat crop growth stages recognition using field images.

Scientific reports
Wheat is one of the world's most widely cultivated cereal crops and is a primary food source for a significant portion of the population. Wheat goes through several distinct developmental phases, and accurately identifying these stages is essential f...

Federated learning with integrated attention multiscale model for brain tumor segmentation.

Scientific reports
Brain tumors are an extremely deadly condition and the growth of abnormal cells that have formed inside the brain causes the illness. According to studies, Magnetic Resonance Imaging (MRI) is a fundamental imaging method that is frequently used in me...