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The differential diagnosis of proliferative breast lesions, benign usual ductal hyperplasia (UDH) versus malignant ductal carcinoma in situ (DCIS) is challenging. This involves a pathologist examining histopathologic sections of a biopsy using a light microscope, evaluating tissue structures for their architecture or size, and assessing individual cell nuclei for their morphology. Imposing diagnostic...
Neural networks are powerful tools for medical image classification and segmentation. However, existing network structures and training procedures assume that the output classes are mutually exclusive and equally important. Many datasets of medical images do not satisfy these conditions. For example, some skin disease datasets have images labelled as coarse-grained class (such as Benign) in addition...
Dermoscopy image is usually used in early diagnosis of malignant melanoma. The diagnosis accuracy by visual inspection is highly relied on the dermatologist's clinical experience. Due to the inaccuracy, subjectivity, and poor reproducibility of human judgement, an automatic recognition algorithm of dermoscopy image is highly desired. In this work, we present a hybrid classification framework for dermoscopy...
In this paper, we propose a multi-view deep residual neural network (mResNet) for the fully automated classification of mammograms as either malignant or normal/benign. Specifically, our mResNet approach consists of an ensemble of deep residual networks (ResNet), which have six input images, including the unregistered craniocaudal (CC) and mediolateral oblique (MLO) mammogram views as well as the...
In wireless capsule endoscopy (WCE), a swallowable miniature optical endoscope is used to transmit color images of the gastrointestinal tract. However, the number of images transmitted is large, taking a significant amount of the medical expert's time to review the scan. In this paper, we propose a technique to automate the abnormality detection in WCE images. We split the image into several patches...
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