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Alexandros Karargyris
IBM
15Publications
4H-index
58Citations
Publications 14
Newest
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 Citations
#1Tanveer Syeda-Mahmood (IBM)H-Index: 20
#2H. Ahmad (IBM)H-Index: 1
Last.Joy T. Wu (IBM)H-Index: 1
view all 12 authors...
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
#1Alexandros Karargyris (IBM)H-Index: 4
#2Ken C. L. Wong (IBM)H-Index: 12
Last.Tanveer Syeda-Mahmood (IBM)H-Index: 20
view all 5 authors...
In many screening applications, the primary goal of a radiologist or assisting artificial intelligence is to rule out certain findings. The classifiers built for such applications are often trained on large datasets that derive labels from clinical notes written for patients. While the quality of the positive findings described in these notes is often reliable, lack of the mention of a finding does not always rule out the presence of it. This happens because radiologists comment on the patient i...
Source
#1Satyananda Kashyap (IBM)H-Index: 1
#2Mehdi Moradi (IBM)H-Index: 17
Last.Tanveer Syeda-Mahmood (IBM)H-Index: 20
view all 8 authors...
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 ...
Source
Age prediction based on appearances of different anatomies in medical images has been clinically explored for many decades. In this paper, we used deep learning to predict a persons age on Chest X-Rays. Specifically, we trained a CNN in regression fashion on a large publicly available dataset. Moreover, for interpretability, we explored activation maps to identify which areas of a CXR image are important for the machine (i.e. CNN) to predict a patients age, offering insight. Overall, amongst cor...
Sep 16, 2018 in MICCAI (Medical Image Computing and Computer-Assisted Intervention)
#1Amin Katouzian (IBM)H-Index: 13
#2Hongzhi Wang (IBM)H-Index: 18
Last.Nassir Navab (TUM: Technische Universität München)H-Index: 61
view all 9 authors...
In this paper, we present a learning based, registration free, atlas ranking technique for selecting outperforming atlases prior to image registration and multi-atlas segmentation (MAS). To this end, we introduce ensemble hashing, where each data (image volume) is represented with ensemble of hash codes and a learnt distance metric is used to obviate the need for pairwise registration between atlases and target image. We then pose the ranking process as an assignment problem and solve it through...
2 CitationsSource
#1Szilárd Vajda (CWU: Central Washington University)H-Index: 12
#2Alexandros Karargyris (IBM)H-Index: 4
Last.George R. Thoma (NIH: National Institutes of Health)H-Index: 27
view all 8 authors...
To detect pulmonary abnormalities such as Tuberculosis (TB), an automatic analysis and classification of chest radiographs can be used as a reliable alternative to more sophisticated and technologically demanding methods (e.g. culture or sputum smear analysis). In target areas like Kenya TB is highly prevalent and often co-occurring with HIV combined with low resources and limited medical assistance. In these regions an automatic screening system can provide a cost-effective solution for a large...
12 CitationsSource
#1Ali Madani (IBM)H-Index: 7
#2Mehdi Moradi (IBM)H-Index: 17
Last.Tanveer Syeda-Mahmood (IBM)H-Index: 20
view all 4 authors...
Deep learning algorithms require large amounts of labeled data which is difficult to attain for medical imaging. Even if a particular dataset is accessible, a learned classifier struggles to maintain the same level of performance on a different medical imaging dataset from a new or never-seen data source domain. Utilizing generative adversarial networks in a semi-supervised learning architecture, we address both problems of labeled data scarcity and data domain overfitting. For cardiac abnormali...
12 CitationsSource
#1Ahmed A. Harouni (IBM)H-Index: 4
#2Alexandros Karargyris (IBM)H-Index: 4
Last.Tanveer Syeda-Mahmood (IBM)H-Index: 20
view all 5 authors...
Medical image processing algorithms have traditionally focused on a specific problem or disease per modality. This approach has continued with the wide-spread adoption of deep learning in the last 5 years. Building a system with multiple neural networks and different specialized image processing algorithms is a challenge as each network requires a lot of memory and is computationally heavy. More importantly, cascading multiple networks propagates errors from one stage to another reducing overall...
2 CitationsSource
#1Zhiyun Xue (NIH: National Institutes of Health)H-Index: 11
#2Stefan Jaeger (NIH: National Institutes of Health)H-Index: 13
Last.George R. Thoma (NIH: National Institutes of Health)H-Index: 27
view all 8 authors...
Chest radiography (CXR) has been used as an effective tool for screening tuberculosis (TB). Because of the lack of radiological expertise in resource-constrained regions, automatic analysis of CXR is appealing as a "first reader". In addition to screening the CXR for disease, it is critical to highlight locations of the disease in abnormal CXRs. In this paper, we focus on the task of locating TB in CXRs which is more challenging due to the intrinsic difficulty of locating the abnormality. The me...
3 CitationsSource
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