AIMC Topic: Image Interpretation, Computer-Assisted

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SCREEN-DR: Collaborative platform for diabetic retinopathy.

International journal of medical informatics
BACKGROUND AND OBJECTIVE: Diabetic retinopathy (DR) is the most prevalent microvascular complication of diabetes mellitus and can lead to irreversible visual loss. Screening programs, based on retinal imaging techniques, are fundamental to detect the...

State of the Art: Machine Learning Applications in Glioma Imaging.

AJR. American journal of roentgenology
OBJECTIVE: Machine learning has recently gained considerable attention because of promising results for a wide range of radiology applications. Here we review recent work using machine learning in brain tumor imaging, specifically segmentation and MR...

Enlarged perivascular spaces in brain MRI: Automated quantification in four regions.

NeuroImage
Enlarged perivascular spaces (PVS) are structural brain changes visible in MRI, are common in aging, and are considered a reflection of cerebral small vessel disease. As such, assessing the burden of PVS has promise as a brain imaging marker. Visual ...

Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images.

IEEE transactions on medical imaging
Prostate cancer is the most common and second most deadly form of cancer in men in the United States. The classification of prostate cancers based on Gleason grading using histological images is important in risk assessment and treatment planning for...

Deep Geodesic Learning for Segmentation and Anatomical Landmarking.

IEEE transactions on medical imaging
In this paper, we propose a novel deep learning framework for anatomy segmentation and automatic landmarking. Specifically, we focus on the challenging problem of mandible segmentation from cone-beam computed tomography (CBCT) scans and identificatio...

Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large-scale multi-sample study.

Human brain mapping
Machine learning is becoming an increasingly popular approach for investigating spatially distributed and subtle neuroanatomical alterations in brain-based disorders. However, some machine learning models have been criticized for requiring a large nu...

Deep Learning to Distinguish Recalled but Benign Mammography Images in Breast Cancer Screening.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: False positives in digital mammography screening lead to high recall rates, resulting in unnecessary medical procedures to patients and health care costs. This study aimed to investigate the revolutionary deep learning methods to distinguish...

Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy.

Nature biomedical engineering
The detection and removal of precancerous polyps via colonoscopy is the gold standard for the prevention of colon cancer. However, the detection rate of adenomatous polyps can vary significantly among endoscopists. Here, we show that a machine-learni...

Infant Brain Development Prediction With Latent Partial Multi-View Representation Learning.

IEEE transactions on medical imaging
The early postnatal period witnesses rapid and dynamic brain development. However, the relationship between brain anatomical structure and cognitive ability is still unknown. Currently, there is no explicit model to characterize this relationship in ...