AIMC Topic: Image Processing, Computer-Assisted

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Using deep-learning algorithms to classify fetal brain ultrasound images as normal or abnormal.

Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
OBJECTIVES: To evaluate the feasibility of using deep-learning algorithms to classify as normal or abnormal sonographic images of the fetal brain obtained in standard axial planes.

Brain tumor classification in MRI image using convolutional neural network.

Mathematical biosciences and engineering : MBE
Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Recent progress in the field of deep learning has helped the health industry in Medical Imaging for Medical Diagnostic of many diseases. For Visual le...

Virtual organelle self-coding for fluorescence imaging via adversarial learning.

Journal of biomedical optics
SIGNIFICANCE: Our study introduces an application of deep learning to virtually generate fluorescence images to reduce the burdens of cost and time from considerable effort in sample preparation related to chemical fixation and staining.

[Artificial intelligence in image analysis-fundamentals and new developments].

Der Hautarzt; Zeitschrift fur Dermatologie, Venerologie, und verwandte Gebiete
BACKGROUND: Since 2017, there have been several reports of artificial intelligence (AI) achieving comparable performance to human experts on medical image analysis tasks. With the first ratification of a computer vision algorithm as a medical device ...

Clinically Applicable Deep Learning Framework for Measurement of the Extent of Hair Loss in Patients With Alopecia Areata.

JAMA dermatology
This study aims to develop a deep learning framework to determine the Severity of Alopecia Tool (SALT) score for measurement of hair loss in patients with alopecia areata.

Artificial Intelligence and Machine Learning in Radiology: Current State and Considerations for Routine Clinical Implementation.

Investigative radiology
Although artificial intelligence (AI) has been a focus of medical research for decades, in the last decade, the field of radiology has seen tremendous innovation and also public focus due to development and application of machine-learning techniques ...

[Computer-assisted skin cancer diagnosis : Is it time for artificial intelligence in clinical practice?].

Der Hautarzt; Zeitschrift fur Dermatologie, Venerologie, und verwandte Gebiete
BACKGROUND: Artificial intelligence (AI) is increasingly being used in medical practice. Especially in the image-based diagnosis of skin cancer, AI shows great potential. However, there is a significant discrepancy between expectations and true relev...

Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence.

Investigative radiology
Radiological images have been assessed qualitatively in most clinical settings by the expert eyes of radiologists and other clinicians. On the other hand, quantification of radiological images has the potential to detect early disease that may be dif...

[Research on brain image segmentation based on deep learning].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Brain image segmentation algorithm based on deep learning is a research hotspot at present. In this paper, firstly, the significance of brain image segmentation and the content of related brain image segmentation algorithm are systematically describe...