DFEN: Dual Feature Enhancement Network for Remote Sensing Image Caption

Autor: Weihua Zhao, Wenzhong Yang, Danny Chen, Fuyuan Wei

Publikováno v: Electronics; Volume 12; Issue 7; Pages: 1547

The remote sensing image caption can acquire ground objects and the semantic relationships between different ground objects. Existing remote sensing image caption algorithms do not acquire enough ground object information from remote-sensing images,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::956ab0e860883cdb416ea69b7fa948fa

Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.

Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.

Web Image Context Extraction with Graph Neural Networks and Sentence Embeddings on the DOM tree

Autor: Chen Dang, Hicham Randrianarivo, Raphaël Fournier-S'Niehotta, Nicolas Audebert

Publikováno v: GEM: Graph Embedding and Mining-ECML/PKDD Workshops
GEM: Graph Embedding and Mining-ECML/PKDD Workshops, Sep 2021, Bilbao, Spain
HAL
Communications in Computer and Information Science ISBN: 9783030937355

International audience; Web Image Context Extraction (WICE) consists in obtaining the textual information describing an image using the content of the surrounding webpage. A common preprocessing step before performing WICE is to render the content of

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4c47a4eb275f03e01d40e819b358e502
https://hal.archives-ouvertes.fr/hal-03324009

Clinically significant prostate cancer detection on MRI with self-supervised learning using image context restoration

Autor: Richard E. Fan, Pejman Ghanouni, Arun Seetharaman, Amir Bolous, Simon John Christoph Soerensen, Mirabela Rusu, Leo C Chen, Indrani Bhattacharya, Geoffrey A. Sonn

Publikováno v: Bolous, A, Seetharaman, A, Bhattacharya, I, Fan, R E, Soerensen, S J C, Chen, L, Ghanouni, P, Sonn, G A & Rusu, M 2021, Clinically significant prostate cancer detection on MRI with self-supervised learning using image context restoration . in M A Mazurowski & K Drukker (eds), Medical Imaging 2021 : Computer-Aided Diagnosis ., 115971M, SPIE-International Society for Optical Engineering, Progress in Biomedical Optics and Imaging-Proceedings of SPIE, vol. 11597, Medical Imaging 2021: Computer-Aided Diagnosis, Virtual, Online, United States, 15/02/2021 . https://doi.org/10.1117/12.2581557

Prostate MRI is increasingly used to help localize and target prostate cancer. Yet, the subtle differences in MRI appearance of cancer compared to normal tissue renders MRI interpretation challenging. Deep learning methods hold promise in automating

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1dc6ffb1d93b095f26e7d469af3d48c4
https://pure.au.dk/portal/da/publications/clinically-significant-prostate-cancer-detection-on-mri-with-selfsupervised-learning-using-image-context-restoration(2eb9b0eb-7193-477a-a0e7-7e38cd95621c).html