AIMC Topic: Image Processing, Computer-Assisted

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Improved gated recurrent unit-based osteosarcoma prediction on histology images: a meta-heuristic-oriented optimization concept.

Scientific reports
The major prevalent primary bone cancer is osteosarcoma. Preoperative chemotherapy is accompanied by resection as part of the normal course of treatment. The diagnosis and treatment of patients are based on the chemotherapy reaction. Contrarily, chem...

Patient-specific MRI super-resolution via implicit neural representations and knowledge transfer.

Physics in medicine and biology
Magnetic resonance imaging (MRI) is a non-invasive imaging technique that provides high soft tissue contrast, playing a vital role in disease diagnosis and treatment planning. However, due to limitations in imaging hardware, scan time, and patient co...

A Deep-Learning Empowered, Real-Time Processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Brain-Computer Interfaces (BCI) and Neurofeedback (NFB) approaches, which both rely on real-time monitoring of brain activity, are increasingly being applied in rehabilitation, assistive technology, neurological diseases and behavioral disorders. Fun...

Impact of harmonization on predicting complications in head and neck cancer after radiotherapy using MRI radiomics and machine learning techniques.

Medical physics
BACKGROUND: Variations in medical images specific to individual scanners restrict the use of radiomics in both clinical practice and research. To create reproducible and generalizable radiomics-based models for outcome prediction and assessment, data...

LEyes: A lightweight framework for deep learning-based eye tracking using synthetic eye images.

Behavior research methods
Deep learning methods have significantly advanced the field of gaze estimation, yet the development of these algorithms is often hindered by a lack of appropriate publicly accessible training datasets. Moreover, models trained on the few available da...

Clinical implications of deep learning based image analysis of whole radical prostatectomy specimens.

Scientific reports
Prostate cancer (PCa) diagnosis faces significant challenges due to its complex pathological characteristics and insufficient pathologist resources. While deep learning-based image analysis (DLIA) shows promise in enhancing diagnostic accuracy, its a...

From inconsistent annotations to ground truth: Aggregation strategies for annotations of proximal carious lesions in dental imagery.

Journal of dentistry
OBJECTIVES: Annotating carious lesions on images is challenging. For artificial intelligence (AI) applications, the aggregation of heterogeneous multi-examiner annotations into one single annotation (e.g. via majority voting, MV) is usually needed. W...

Improving realism in abdominal ultrasound simulation combining a segmentation-guided loss and polar coordinates training.

Medical physics
BACKGROUND: Ultrasound (US) simulation helps train physicians and medical students in image acquisition and interpretation, enabling safe practice of transducer manipulation and organ identification. Current simulators generate realistic images from ...

VisionGuard: enhancing diabetic retinopathy detection with hybrid deep learning.

Expert review of medical devices
OBJECTIVES: Early detection of diabetic retinopathy (DR) and timely intervention are critical for preventing vision loss. Recently, deep learning techniques have shown promising results in streamlining this process. The objective of this study was to...

A review of denoising methods in single-particle cryo-EM.

Micron (Oxford, England : 1993)
Cryo-EM has become a vital technique for resolving macromolecular structures at near-atomic resolution, enabling the visualization of proteins and large molecular complexes. However, the images are often accompanied by extremely low SNR, which poses ...