Beddiar, D. R., Oussalah, M., Seppänen, T., & Jennane, R. (2022). ACapMed: Automatic Captioning for Medical Imaging. Applied Sciences, 12(21), 11092. https://doi.org/10.3390/app122111092
ACapMed : automatic captioning for medical imaging
|Author:||Beddiar, Djamila Romaissa1; Oussalah, Mourad1,2; Seppänen, Tapio1;|
1Center for Machine Vision and Signal Analysis, University of Oulu, FI-90014 Oulu, Finland
2Faculty of Medicine, University of Oulu, FI-90014 Oulu, Finland
3IDP Laboratory—UMR CNRS 7013, University of Orleans, CEDEX 2, 45067 Orleans, France
|Online Access:||PDF Full Text (PDF, 9.9 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022112266501
Multidisciplinary Digital Publishing Institute,
|Publish Date:|| 2022-11-22
Medical image captioning is a very challenging task that has been rarely addressed in the literature on natural image captioning. Some existing image captioning techniques exploit objects present in the image next to the visual features while generating descriptions. However, this is not possible for medical image captioning when one requires following clinician-like explanations in image content descriptions. Inspired by the preceding, this paper proposes using medical concepts associated with images, in accordance with their visual features, to generate new captions. Our end-to-end trainable network is composed of a semantic feature encoder based on a multi-label classifier to identify medical concepts related to images, a visual feature encoder, and an LSTM model for text generation. Beam search is employed to ensure the best selection of the next word for a given sequence of words based on the merged features of the medical image. We evaluated our proposal on the ImageCLEF medical captioning dataset, and the results demonstrate the effectiveness and efficiency of the developed approach.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
114 Physical sciences
116 Chemical sciences
119 Other natural sciences
214 Mechanical engineering
This work is supported by the Academy of Finland Profi5 DigiHealth project (#326291), which is gratefully acknowledged.
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).