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pro vyhledávání: '"Pranshu Ranjan Singh"'
Autor:
Qiao ZhongZheng, ArulMurugan Ambikapathi, Pranshu Ranjan Singh, Savitha Ramasamy, Saisubramaniam Gopalakrishnan, Ponnuthurai Nagaratnam Suganthan
Publikováno v:
2021 IEEE International Conference on Image Processing (ICIP).
Autor:
Saisubramaniam Gopalakrishnan, Pranshu Ranjan Singh, Haytham Fayek, Savitha Ramasamy, ArulMurugan Ambikapathi
Deep neural networks have shown promise in several domains, and the learned data (task) specific information is implicitly stored in the network parameters. Extraction and utilization of encoded knowledge representations are vital when data is no lon
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4634e6469e3c21f5e960713482e29882
http://arxiv.org/abs/2012.06789
http://arxiv.org/abs/2012.06789
Publikováno v:
ICIP
Quantitative measurements obtained from medical images guide clinicians in several use cases but manually obtaining such measurements are both laborious and subject to inter-observer variations. We develop a hybrid deep reinforced regression framewor
Autor:
Pranshu Ranjan Singh, Vijay Chandrasekhar, Yasin Yazici, ArulMurugan Ambikapathi, Chuan-Sheng Foo, Saisubramaniam Gopalakrishnan
Utilization of classification latent space information for downstream reconstruction and generation is an intriguing and a relatively unexplored area. In general, discriminative representations are rich in class specific features but are too sparse f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a8c62718d4e766896c450f9aff0f21f7
Publikováno v:
Biomedical Signal Processing and Control. 57:101782
The major challenge in applying deep neural network techniques in the medical imaging domain is how to cope with small datasets and the limited amount of annotated samples. Data augmentation procedures that include conventional geometrical transforma