Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Ericheva, Elena"'
Autor:
Wijk, Hjalmar, Lin, Tao, Becker, Joel, Jawhar, Sami, Parikh, Neev, Broadley, Thomas, Chan, Lawrence, Chen, Michael, Clymer, Josh, Dhyani, Jai, Ericheva, Elena, Garcia, Katharyn, Goodrich, Brian, Jurkovic, Nikola, Kinniment, Megan, Lajko, Aron, Nix, Seraphina, Sato, Lucas, Saunders, William, Taran, Maksym, West, Ben, Barnes, Elizabeth
Frontier AI safety policies highlight automation of AI research and development (R&D) by AI agents as an important capability to anticipate. However, there exist few evaluations for AI R&D capabilities, and none that are highly realistic and have a d
Externí odkaz:
http://arxiv.org/abs/2411.15114
Autor:
Drokin, Ivan, Ericheva, Elena
This paper proposes novel end-to-end framework for detecting suspicious pulmonary nodules in chest CT scans. The method core idea is a new nodule segmentation architecture with a model-based feature projection block on three-dimensional convolutions.
Externí odkaz:
http://arxiv.org/abs/2106.05741
Autor:
Drokin, Ivan, Ericheva, Elena
Publikováno v:
In: van der Aalst W.M.P. et al. (eds) Analysis of Images, Social Networks and Texts. AIST 2020. Lecture Notes in Computer Science, vol 12602. Springer, Cham
This paper focuses on a novel approach for false-positive reduction (FPR) of nodule candidates in Computer-aided detection (CADe) systems following the suspicious lesions detection stage. Contrary to typical decisions in medical image analysis, the p
Externí odkaz:
http://arxiv.org/abs/2005.03654
We propose a novel method to improve deep learning model performance on highly-imbalanced tasks. The proposed method is based on CycleGAN to achieve balanced dataset. We show that data augmentation with GAN helps to improve accuracy of pneumonia bina
Externí odkaz:
http://arxiv.org/abs/1908.00433