Glaucoma is a leading cause of permanent blindness. ARGALI, an automated system for glaucoma detection, employs several methods for segmenting the optic cup and disc from retinal images, combined using a fusion network, to determine the cup to disc ratio (CDR), an important clinical indicator of glaucoma. This paper discusses the use of SVM as an alternative fusion strategy in ARGALI, and evaluates its performance against the component methods and neural network (NN) fusion in the CDR calculation. The results show SVM and NN provide similar improvements over the component methods, but with SVM having a greater consistency over the NN, suggesting potential for SVM as a viable option in ARGALI.

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This paper demonstrates PERC-our system for crowdsourced entity resolution with human errors. Entity Resolution (ER) is a critical step in data cleaning and analytics. Although many machinebased methods existed for ER task, crowdsourcing is becoming increasingly important since humans can provide more insightful information for complex tasks, e.g., clustering of images and natural language processing. However, human workers still make mistakes due to lack of domain expertise or seriousness, ambiguity, or even malicious intent. To this end, we present a system, called PERC (probabilistic entity resolution with crowd errors), which adopts an uncertain graph model to address the entity resolution problem with noisy crowd answers. Using our framework, the problem of ER becomes equivalent to finding the maximum-likelihood clustering. In particular, we propose a novel metric called "reliability" to measure the quality of a clustering, which takes into account both the connected-ness inside and across all clusters. PERC then automatically selects the next question to ask the crowd that maximally increases the "reliability" of the current clustering. This demonstration highlights (1) a reliability-based next crowdsourcing framework for crowdsourced ER, which does not require any user-defined threshold, and no apriori information about the error rate of the crowd workers, (2) it improves the ER quality by 15% and reduces the crowdsourcing cost by 50% compared to state-ofthe-art methods, and (3) its GUI can interact with users to help them compare different crowdsourced ER algorithms, their intermediate ER results as they progress, and their selected next crowdsourcing questions in a user-friendly manner. Our demonstration video is at: https://www.youtube.com/watch?v=rQ7nu3b8zXY.