| Full text | |
| Author(s): |
Braz, Leodecio
;
Teixeira, Vinicius
;
Pedrini, Helio
;
Dias, Zanoni
;
Setubal, JC
;
Silva, WM
Total Authors: 6
|
| Document type: | Journal article |
| Source: | ADVANCES IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY, BSB 2020; v. 12558, p. 12-pg., 2020-01-01. |
| Abstract | |
Deep learning techniques have been increasingly applied to provide more accurate results in the classification of medical images and in the classification and generation of report texts. The main objective of this paper is to investigate the influence of fusing several features of heterogeneous modalities to improve musculoskeletal abnormality detection in comparison with the individual results of image and text classification. In this work, we propose a novel image-text classification framework, named ImTeNet, to learn relevant features from image and text information for binary classification of musculoskeletal radiography. Initially, we use a caption generator model to artificially create textual data for a dataset lacking text information. Then, we apply the ImTeNet, a multi-modal information model that consists of two distinct networks, DenseNet-169 and BERT, to perform image and text classification tasks respectively, and a fusion module that receives a concatenation of feature vectors extracted from both. To evaluate our proposed approach, we used the Musculoskeletal Radiographs (MURA) dataset and compare the results obtained with image and text classification scheme individually. (AU) | |
| FAPESP's process: | 19/20875-8 - Chest X-ray image classification using deep neural networks |
| Grantee: | Vinicius Teixeira de Melo |
| Support Opportunities: | Scholarships in Brazil - Master |
| FAPESP's process: | 17/12646-3 - Déjà vu: feature-space-time coherence from heterogeneous data for media integrity analytics and interpretation of events |
| Grantee: | Anderson de Rezende Rocha |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 15/11937-9 - Investigation of hard problems from the algorithmic and structural stand points |
| Grantee: | Flávio Keidi Miyazawa |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 17/16246-0 - Sensitive media analysis through deep learning architectures |
| Grantee: | Sandra Eliza Fontes de Avila |
| Support Opportunities: | Regular Research Grants |