Lesion Segmentation in Ultrasound Using Semi-pixel-wise Cycle Generative Adversarial Nets

Autor: Remco Duits, Biyuan Wang, Aiwen Zheng, Tao Tan, Farhad Ghazvinian Zanjani, Yuji Qi, Bingbin Yu, Zheren Li, Jie Xing

Publikováno v: IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(6), 2555-2565. Institute of Electrical and Electronics Engineers

Breast cancer is the most common invasive cancer with the highest cancer occurrence in females. Handheld ultrasound is one of the most efficient ways to identify and diagnose the breast cancer. The area and the shape information of a lesion is very h

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::03a96cbbfcf0d78b63f995edcb79b196
https://research.tue.nl/en/publications/cbd9783a-dc08-476f-9d58-592d38cea38b

From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge

Autor: Jörg Franke, Keisuke Fukuta, Hao Chen, Willem Vreuls, Aoxiao Zhong, Farhad Ghazvinian Zanjani, Svitlana Zinger, Richard J. Chen, Hunter Jackson, Fabian Both, Heidi V.N. Küsters-Vandevelde, Daisuke Komura, Babak Ehteshami Bejnordi, Marcory C. R. F. van Dijk, Bram van Ginneken, Eren Halici, Ludwig Jacobsson, Vlado Ovtcharov, Quanzheng Li, Jeroen van der Laak, Peter Bult, Oscar Geessink, Melih cetin, Shaoqun Zeng, Geert Litjens, Martin Hedlund, Anders Bjorholm Dahl, Byungjae Lee, Péter Bándi, Huangjing Lin, Jeppe Thagaard, Quirine F. Manson, Meyke Hermsen, Shenghua Cheng, Kyunghyun Paeng, Maschenka Balkenhol

Publikováno v: Bandi, P, Geessink, O, Manson, Q, van Dijk, M, Balkenhol, M, Hermsen, M, Bejnordi, B E, Lee, B, Paeng, K, Zhong, A, Li, Q, Zanjani, F G, Zinger, S, Fukuta, K, Komura, D, Ovtcharov, V, Cheng, S, Zeng, S, Thagaard, J, Dahl, A B, Lin, H, Chen, H, Jacobsson, L, Hedlund, M, Cetin, M, Halici, E, Jackson, H, Chen, R, Both, F, Franke, J, Kusters-Vandevelde, H, Vreuls, W, Bult, P, van Ginneken, B, van der Laak, J & Litjens, G 2018, ' From detection of individual metastases to classification of lymph node status at the patient level: the CAMELYON17 challenge ', I E E E Transactions on Medical Imaging, vol. 38, no. 2, pp. 550-560 . https://doi.org/10.1109/TMI.2018.2867350
IEEE Transactions on Medical Imaging, 38, 2, pp. 550-560
IEEE Transactions on Medical Imaging, 38, 550-560
IEEE Transactions on Medical Imaging, 38(2):8447230, 550-560. Institute of Electrical and Electronics Engineers

Automated detection of cancer metastases in lymph nodes has the potential to improve the assessment of prognosis for patients. To enable fair comparison between the algorithms for this purpose, we set up the CAMELYON17 challenge in conjunction with t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::05a601529cc742865fed64af18995ac2
https://doi.org/10.1109/tmi.2018.2867350

Robust and semantic needle detection in 3D ultrasound using orthogonal-plane convolutional neural networks

Autor: Arash Pourtaherian, Farhad Ghazvinian Zanjani, Svitlana Zinger, Nenad Mihajlovic, Gary C. Ng, Hendrikus H. M. Korsten, Peter H. N. de With

Publikováno v: International Journal of Computer Assisted Radiology and Surgery, 13(9), 1321-1333. Springer
International Journal of Computer Assisted Radiology and Surgery

Purpose: During needle interventions, successful automated detection of the needle immediately after insertion is necessary to allow the physician identify and correct any misalignment of the needle and the target at early stages, which reduces needl

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9418e278a7702cb53ff56e1ab872f57a
http://www.scopus.com/inward/record.url?scp=85047919138&partnerID=8YFLogxK

Impact of JPEG 2000 compression on deep convolutional neural networks for metastatic cancer detection in histopathological images

Autor: Farhad Ghazvinian Zanjani, Svitlana Zinger, Saeed Mahmoudpour, Bastiaan Piepers, Peter Schelkens

Publikováno v: Journal of Medical Imaging, 6(2):027501. SPIE

The availability of massive amounts of data in histopathological whole-slide images (WSIs) has enabled the application of deep learning models and especially convolutional neural networks (CNNs), which have shown a high potential for improvement in c

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b1845081ce3b7227e8ce8a60c56e7261
https://biblio.vub.ac.be/vubir/impact-of-jpeg-2000-compression-on-deep-convolutional-neural-networks-for-metastatic-cancer-detection-in-histopathological-images(4fd62f3e-915d-4baf-ac61-8e3d9104bbc6).html

Stain normalization of histopathology images using generative adversarial networks

Autor: Farhad Ghazvinian Zanjani, Svitlana Zinger, Babak Ehteshami Bejnordi, Jeroen A W M van der Laak, Peter H. N. de With

Publikováno v: 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018, 2018-April, 573-577
ISBI

Computational histopathology involves CAD for microscopic analysis of stained histopathological slides to study presence, localization or grading of disease. An important stage in a CAD system, stain color normalization, has been broadly studied. The

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e06e6dc056082f12e5d84b7f68c1ac80
https://research.tue.nl/nl/publications/24b938bb-fb19-491c-b25f-5d6b1b68d066