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RADnet: Radiologist level accuracy using deep learning for hemorrhage detection in CT scans.

, , , and . ISBI, page 281-284. IEEE, (2018)

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Semi-supervised Learning for Dense Object Detection in Retail Scenes., , and . CoRR, (2021)Deep Semantics for Explainable Visuospatial Intelligence: Perspectives on Integrating Commonsense Spatial Abstractions and Low-Level Neural Features., , and . NeSy@IJCAI, (2019)Benchmark for Generic Product Detection: A Low Data Baseline for Dense Object Detection., , and . ICIAR (1), volume 12131 of Lecture Notes in Computer Science, page 30-41. Springer, (2020)Benchmark for Generic Product Detection: A strong baseline for Dense Object Detection., , and . CoRR, (2019)Semantic Analysis of (Reflectional) Visual Symmetry: A Human-Centred Computational Model for Declarative Explainability., , , , and . CoRR, (2018)Multidomain Document Layout Understanding using Few Shot Object Detection., , , and . CoRR, (2018)Commonsense Visual Sensemaking for Autonomous Driving: On Generalised Neurosymbolic Online Abduction Integrating Vision and Semantics., , and . CoRR, (2020)Driven by Commonsense., , and . ECAI, volume 325 of Frontiers in Artificial Intelligence and Applications, page 2939-2940. IOS Press, (2020)Detection of Tooth caries in Bitewing Radiographs using Deep Learning., , , and . CoRR, (2017)RADNET: Radiologist Level Accuracy using Deep Learning for HEMORRHAGE detection in CT Scans., , , and . CoRR, (2017)