AIMC Topic: Image Processing, Computer-Assisted

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Hybrid data augmentation strategies for robust deep learning classification of corneal topographic maptopographic map.

Biomedical physics & engineering express
Deep learning has emerged as a powerful tool in medical imaging, particularly for corneal topographic map classification. However, the scarcity of labeled data poses a significant challenge to achieving robust performance. This study investigates the...

Generative artificial intelligence enables the generation of bone scintigraphy images and improves generalization of deep learning models in data-constrained environments.

European journal of nuclear medicine and molecular imaging
PURPOSE: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generat...

Segmentation of coronary artery and calcification using prior knowledge based deep learning framework.

Medical physics
BACKGROUND: Computed tomography angiography (CTA) is used to screen for coronary artery calcification. As the coronary artery has complicated structure and tiny lumen, manual screening is a time-consuming task. Recently, many deep learning methods ha...

Deep Learning-Based Precontrast CT Parcellation for MRI-Free Brain Amyloid PET Quantification.

Clinical nuclear medicine
PURPOSE: This study aimed to develop a deep learning (DL) model for brain region parcellation using CT data from PET/CT scans to enable accurate amyloid quantification in 18 F-FBB PET/CT without relying on high-resolution MRI.

Radiogenomic explainable AI with neural ordinary differential equation for identifying post-SRS brain metastasis radionecrosis.

Medical physics
BACKGROUND: Stereotactic radiosurgery (SRS) is widely used for managing brain metastases (BMs), but an adverse effect, radionecrosis, complicates post-SRS management. Differentiating radionecrosis from tumor recurrence non-invasively remains a major ...

Deep learning image registration for cardiac motion estimation in adult and fetal echocardiography via a focus on anatomic plausibility and texture quality of warped image.

Computers in biology and medicine
Temporal echocardiography image registration is important for cardiac motion estimation, myocardial strain assessments, and stroke volume quantifications. Deep learning image registration (DLIR) is a promising way to achieve consistent and accurate r...

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...

Artificial intelligence for image recognition in diagnosing oral and oropharyngeal cancer and leukoplakia.

Scientific reports
Visual diagnosis is one of the key features of squamous cell carcinoma of the oral cavity (OSCC) and oropharynx (OPSCC), both subsets of head and neck squamous cell carcinoma (HNSCC) with a heterogeneous clinical appearance. Advancements in artificia...

Self-critical strategy adjustment based artificial intelligence method in generating diagnostic reports of respiratory diseases.

Physiological measurement
. Humanity faces many health challenges, among which respiratory diseases are one of the leading causes of human death. Existing AI-driven pre-diagnosis approaches can enhance the efficiency of diagnosis but still face challenges. For example, single...