A deep learning system transforms a 3D image of a pulmonary nodule from a CT scan into a low-dimensional embedding vector… We demonstrate that such a vector representation preserves semantic information about the nodule and offers a viable approach for content-based image retrieval (CBIR) We discuss the theoretical limitations of the available datasets and overcome them by applying transfer learning of the state-of-the-art lung nodule detection model . We evaluate the system using the LIDC-IDRI dataset of thoracic CT scans . We devise a similarity score and show that it can be utilized to measure similarity between annotations of the same nodule by different radiologists and the top four CBIR results . A comparison between doctors and algorithm scores suggests that the benefit provided by the system to the radiologist end-user is comparable to obtaining a second radiologist’s

Links: PDF - Abstract

Code :

None

Keywords : nodule - system - learning - retrieval - cbir -

Leave a Reply

Your email address will not be published. Required fields are marked *

error

Enjoy this blog? Please spread the word :)