Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation

By Julian Georg Zilly, Joachim M. Buhmann, D. Mahapatra. Comput. Medical Imaging Graph., 2017

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Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network (2017, methodology): “For the task of optic cup segmentation, we compare with the method from [11] (further referred as BCF, as the name of the paper suggests) and from [17].”
Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation (2018, methodology): “In [29], an ensemble learning method is proposed to extract OC and OD based on the CNN architecture.”
IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge. (2020, background): “…channels to other hand-crafted features or local saliency maps for detection of abnormalities associated with DR ( Chudzik et al., 2018; Orlando et al., 2018; Dai et al., 2018 ), segmentation of OD ( Zilly et al., 2017; Fu et al., 2018 ), and detection of DR ( Rangrej and Sivaswamy, 2017 ).”
Thoracic Disease Identification and Localization with Limited Supervision (2017, background): “In clinical practice, visual evidence that supports the classification result, such as spatial localization [2] or segmentation [35, 38] of sites of abnormalities is an indispensable part of clinical diagnosis which provides interpretation and insights.”