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Pathological myopia is a leading cause of visual impairment, and can lead to blindness in children if left undetected. We present a bag-of-feature and sparse learning based framework to automatically recognize pathological myopia in retinal fundus images and discover the most related visual features corresponding to the retinal changes in pathological myopia. In the learning phase, the codebook for...
We explore feature selection methodology for automatic Pathological Myopia detection via learning from an optimal set of features. An mRMR optimized classifier is trained using the candidate feature set to find the optimized classifier. We tested the proposed methodology on eye records of approximately 800 subjects collected from a population study. The experimental results demonstrate that the new...
Automatic medical image classification is difficult because of the lacking of training data. As manual labeling is too costly, we provide an automatic labeling solution to this problem by making use of the radiology report associated with the medical images. We first segment and reconstruct the 3D regions of interest (ROIs) from the medical images, and extract pathology and anatomy information from...
Large number of medical images are produced daily in hospitals and medical institutions, the needs to efficiently process, index, search and retrieve these images are great. In this paper, we propose a pathology based medical image annotation framework using a statistical machine translation approach. After pathology terms and regions of interest (ROIs) are extracted from training text and images...
Myopia is a growing concern in many societies. In extremely high myopia, pathological myopia, which can cause visual loss, can occur. Pathological myopia is also accompanied by various visually perceivable symptoms on the retina, such as peripapillary atrophy. PAMELA is an automatic system for the detection of pathological myopia through the presence of peripapillary atrophy. In this paper, we describe...
In intracranial pathological examinations using CT scan, brain midline shift (MLS) is an important diagnostic feature indicating the pathological severity and patient's survival possibility. In this paper, we develop a new method of tracing the brain midline shift in traumatic brain injury (TBI) CT images using its original cause - the hemorrhage. Firstly, we model the relationship between the hemorrhage...
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