AIMC Topic: Diagnostic Imaging

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Machine learning in point-of-care automated classification of oral potentially malignant and malignant disorders: a systematic review and meta-analysis.

Scientific reports
Machine learning (ML) algorithms are becoming increasingly pervasive in the domains of medical diagnostics and prognostication, afforded by complex deep learning architectures that overcome the limitations of manual feature extraction. In this system...

Dermoscopy and skin imaging light sources: a comparison and review of spectral power distribution and color consistency.

Journal of biomedical optics
SIGNIFICANCE: Dermoscopes incorporate light, polarizers, and optical magnification into a handheld tool that is commonly used by dermatologists to evaluate skin findings. Diagnostic accuracy is improved when dermoscopes are used, and some major artif...

Addressing fairness in artificial intelligence for medical imaging.

Nature communications
A plethora of work has shown that AI systems can systematically and unfairly be biased against certain populations in multiple scenarios. The field of medical imaging, where AI systems are beginning to be increasingly adopted, is no exception. Here w...

Deep learning-based noise filtering toward millisecond order imaging by using scanning transmission electron microscopy.

Scientific reports
Application of scanning transmission electron microscopy (STEM) to in situ observation will be essential in the current and emerging data-driven materials science by taking STEM's high affinity with various analytical options into account. As is well...

Computer-aided anatomy recognition in intrathoracic and -abdominal surgery: a systematic review.

Surgical endoscopy
BACKGROUND: Minimally invasive surgery is complex and associated with substantial learning curves. Computer-aided anatomy recognition, such as artificial intelligence-based algorithms, may improve anatomical orientation, prevent tissue injury, and im...

A systematic review on the use of artificial intelligence in gynecologic imaging - Background, state of the art, and future directions.

Gynecologic oncology
OBJECTIVE: Machine learning, deep learning, and artificial intelligence (AI) are terms that have made their way into nearly all areas of medicine. In the case of medical imaging, these methods have become the state of the art in nearly all areas from...

Combined Mueller matrix imaging and artificial intelligence classification framework for Hepatitis B detection.

Journal of biomedical optics
SIGNIFICANCE: The combination of polarized imaging with artificial intelligence (AI) technology has provided a powerful tool for performing an objective and precise diagnosis in medicine.

Interpretability-Guided Inductive Bias For Deep Learning Based Medical Image.

Medical image analysis
Deep learning methods provide state of the art performance for supervised learning based medical image analysis. However it is essential that trained models extract clinically relevant features for downstream tasks as, otherwise, shortcut learning an...

Bridging 3D Slicer and ROS2 for Image-Guided Robotic Interventions.

Sensors (Basel, Switzerland)
Developing image-guided robotic systems requires access to flexible, open-source software. For image guidance, the open-source medical imaging platform 3D Slicer is one of the most adopted tools that can be used for research and prototyping. Similarl...