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Computer Sciences

Research Collection School Of Computing and Information Systems

Medical imaging

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Full-Text Articles in Physical Sciences and Mathematics

Medical Imaging Specialists And 3d: A Domain Perspective On Mobile 3d Interactions, Teddy Seyed, Frank Maurer, Francisco Marinho Rodrigues, Anthony Tang May 2014

Medical Imaging Specialists And 3d: A Domain Perspective On Mobile 3d Interactions, Teddy Seyed, Frank Maurer, Francisco Marinho Rodrigues, Anthony Tang

Research Collection School Of Computing and Information Systems

3D volumetric medical images, such as MRIs, are commonly explored and interacted with by medical imaging experts using systems that require keyboard and mouse-based techniques. These techniques have presented challenges for medical imaging specialists: 3D spatial navigation is difficult, in addition to the detailed selection and analysis of 3D medical images being difficult due to depth perception and occlusion issues. In this work, we explore a potential solution to these challenges by using tangible interaction techniques with a mobile device to simplify 3D interactions for medical imaging specialists. We discuss preliminary observations from our design sessions with medical imaging specialists …


Batch Mode Active Learning And Its Applications To Medical Image Classification, Steven C. H. Hoi, Rong Jin, Jianke Zhu, Michael R. Lyu Jun 2006

Batch Mode Active Learning And Its Applications To Medical Image Classification, Steven C. H. Hoi, Rong Jin, Jianke Zhu, Michael R. Lyu

Research Collection School Of Computing and Information Systems

The goal of active learning is to select the most informative examples for manual labeling. Most of the previous studies in active learning have focused on selecting a single unlabeled example in each iteration. This could be inefficient since the classification model has to be retrained for every labeled example. In this paper, we present a framework for "batch mode active learning" that applies the Fisher information matrix to select a number of informative examples simultaneously. The key computational challenge is how to efficiently identify the subset of unlabeled examples that can result in the largest reduction in the Fisher …