Satyananda Kashyap
Publications 3
#1Tanveer Syeda-Mahmood (IBM)H-Index: 20
#2H. Ahmad (IBM)H-Index: 1
Last.Joy T. Wu (IBM)H-Index: 1
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Chest X-rays are the most common diagnostic exams in emergency rooms and hospitals. There has been a surge of work on automatic interpretation of chest X-rays using deep learning approaches after the availability of large open source chest X-ray dataset from NIH. However, the labels are not sufficiently rich and descriptive for training classification tools. Further, it does not adequately address the findings seen in Chest X-rays taken in anterior-posterior (AP) view which also depict the place...
1 CitationsSource
#1Satyananda Kashyap (IBM)H-Index: 1
#2Mehdi Moradi (IBM)H-Index: 17
Last.Tanveer Syeda-Mahmood (IBM)H-Index: 20
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Chest X-rays are among the most common modalities in medical imaging. Technical flaws of these images, such as over- or under-exposure or wrong positioning of the patients can result in a decision to reject and repeat the scan. We propose an automatic method to detect images that are not suitable for diagnostic study. If deployed at the point of image acquisition, such a system can warn the technician, so the repeat image is acquired without the need to bring the patient back to the scanner. We ...
#1Jayaraman J. Thiagarajan (LLNL: Lawrence Livermore National Laboratory)H-Index: 14
#2Satyananda Kashyap (IBM)H-Index: 1
Last.Alexandros Karargyris (IBM)H-Index: 4
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Weakly supervised instance labeling using only image-level labels, in lieu of expensive fine-grained pixel annotations, is crucial in several applications including medical image analysis. In contrast to conventional instance segmentation scenarios in computer vision, the problems that we consider are characterized by a small number of training images and non-local patterns that lead to the diagnosis. In this paper, we explore the use of multiple instance learning (MIL) to design an instance lab...