Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Elena Ericheva"'
Autor:
Elena Ericheva, Ivan Drokin
Publikováno v:
Machine Learning in Medical Imaging ISBN: 9783030875886
MLMI@MICCAI
MLMI@MICCAI
This paper proposes novel end-to-end framework for detecting suspicious pulmonary nodules in chest CT scans. The method’s core idea is a new nodule segmentation architecture with a model-based feature projection block on three-dimensional convoluti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::045b6d1bcc363f95752dc6673a9145b3
https://doi.org/10.1007/978-3-030-87589-3_10
https://doi.org/10.1007/978-3-030-87589-3_10
Autor:
Ivan Drokin, Elena Ericheva
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030726096
AIST
AIST
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:
https://explore.openaire.eu/search/publication?articleId=doi_________::2542d9647d484ca1f87e602bdb48c517
https://doi.org/10.1007/978-3-030-72610-2_15
https://doi.org/10.1007/978-3-030-72610-2_15
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030373337
AIST
AIST
When one applies machine learning to a real-world problem, sometimes data imbalance makes a crucial impact on the resulting model’s performance. We propose to use generative adversarial network (GAN) to do data balancing through data augmentation i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e34cf547c90e90007b0ed13f343b4c0c
https://doi.org/10.1007/978-3-030-37334-4_29
https://doi.org/10.1007/978-3-030-37334-4_29