ORCID Profile
0000-0002-5759-4896
Current Organisations
Centre Hospitalier Universitaire Vaudois
,
École Polytechnique Fédérale de Lausanne
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Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: IEEE
Date: 13-04-2021
Publisher: IEEE
Date: 02-2011
Publisher: IEEE
Date: 11-2013
Publisher: IEEE
Date: 2023
Publisher: Springer International Publishing
Date: 2020
Publisher: IEEE
Date: 10-2012
Publisher: IEEE
Date: 06-2020
Publisher: Springer International Publishing
Date: 2017
Publisher: IEEE
Date: 04-2017
Publisher: Elsevier BV
Date: 03-1970
Publisher: IEEE
Date: 12-2010
Publisher: IEEE
Date: 10-2019
Publisher: Springer International Publishing
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: IEEE
Date: 10-2014
Publisher: Springer International Publishing
Date: 2020
Publisher: IEEE
Date: 10-2019
Publisher: Springer International Publishing
Date: 2018
Publisher: IEEE
Date: 04-2017
Publisher: IEEE
Date: 06-2021
Publisher: Elsevier BV
Date: 10-2023
Publisher: Springer Science and Business Media LLC
Date: 18-07-2019
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Springer International Publishing
Date: 2021
Publisher: Springer International Publishing
Date: 2021
Publisher: IEEE
Date: 2021
Publisher: IEEE
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2018
Publisher: Elsevier BV
Date: 08-2019
Publisher: Springer International Publishing
Date: 2021
Publisher: Elsevier BV
Date: 2019
DOI: 10.1016/J.COMPMEDIMAG.2018.10.005
Abstract: Anatomical landmark segmentation and pathology localisation are important steps in automated analysis of medical images. They are particularly challenging when the anatomy or pathology is small, as in retinal images (e.g. vasculature branches or microaneurysm lesions) and cardiac MRI, or when the image is of low quality due to device acquisition parameters as in magnetic resonance (MR) scanners. We propose an image super-resolution method using progressive generative adversarial networks (P-GANs) that can take as input a low-resolution image and generate a high resolution image of desired scaling factor. The super resolved images can be used for more accurate detection of landmarks and pathologies. Our primary contribution is in proposing a multi-stage model where the output image quality of one stage is progressively improved in the next stage by using a triplet loss function. The triplet loss enables stepwise image quality improvement by using the output of the previous stage as the baseline. This facilitates generation of super resolved images of high scaling factor while maintaining good image quality. Experimental results for image super-resolution show that our proposed multi stage P-GAN outperforms competing methods and baseline GANs. The super resolved images when used for landmark and pathology detection result in accuracy levels close to those obtained when using the original high resolution images. We also demonstrate our methods effectiveness on magnetic resonance (MR) images, thus establishing its broader applicability.
Publisher: IEEE
Date: 09-2015
Publisher: IBM
Date: 07-2017
Publisher: Frontiers Media SA
Date: 09-06-2020
Publisher: Elsevier BV
Date: 07-2019
Publisher: IEEE
Date: 05-2019
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: IEEE
Date: 11-2014
Publisher: Springer International Publishing
Date: 2016
Publisher: IEEE
Date: 11-2015
Publisher: IEEE
Date: 06-2019
Publisher: Elsevier BV
Date: 07-2020
Publisher: IEEE
Date: 10-2021
Publisher: IEEE
Date: 10-2021
Publisher: IEEE
Date: 10-2021
Publisher: IEEE
Date: 05-2019
Publisher: Elsevier BV
Date: 07-2022
DOI: 10.1016/J.MEDIA.2022.102473
Abstract: Supervised learning is constrained by the availability of labeled data, which are especially expensive to acquire in the field of digital pathology. Making use of open-source data for pre-training or using domain adaptation can be a way to overcome this issue. However, pre-trained networks often fail to generalize to new test domains that are not distributed identically due to tissue stainings, types, and textures variations. Additionally, current domain adaptation methods mainly rely on fully-labeled source datasets. In this work, we propose Self-Rule to Multi-Adapt (SRMA), which takes advantage of self-supervised learning to perform domain adaptation, and removes the necessity of fully-labeled source datasets. SRMA can effectively transfer the discriminative knowledge obtained from a few labeled source domain's data to a new target domain without requiring additional tissue annotations. Our method harnesses both domains' structures by capturing visual similarity with intra-domain and cross-domain self-supervision. Moreover, we present a generalized formulation of our approach that allows the framework to learn from multiple source domains. We show that our proposed method outperforms baselines for domain adaptation of colorectal tissue type classification in single and multi-source settings, and further validate our approach on an in-house clinical cohort. The code and trained models are available open-source: hristianabbet/SRA.
Publisher: Elsevier BV
Date: 04-2020
No related grants have been discovered for Behzad Bozorgtabar.