Graduate School and Research Center in Digital Sciences

Enhancing endoscopic image classification with symptom localization and data augmentation

Hoang, Trung-Hieu; Nguyen, Hai-Dang; Nguyen, Viet-Anh; Nguyen, Thanh-An; Nguyen, Vinh-Tiep; Tran, Minh-Triet

MM 2019, 27th ACM International Conference on Multimedia, October 21-25, 2019, Nice, France

Inspired by recent advances in computer vision and deep learning, we propose new enhancements to tackle problems appearing in endoscopic image analysis, especially abnormality finding and anatomical landmark detection. In details, a combination of Residual Neural Network and Faster R-CNN are jointly applied in order to take all of their advantages and improve the overall performance. Nevertheless, novel data augmentation is designed and adapted to corresponding domains. Our approaches prove their competitive results in term of not only the accuracy but also the inference time in Medico: The 2018 Multimedia for Medicine Task and The Biomedia ACM MM Grand Challenge 2019. These results show the great potential of the collaborating between deep learning models and data augmentation in medical image analysis applications. Especially, more than 4900 bounding boxes localizing the symptom of some classes from KVASIR dataset that we annotated and used in this project are shared online for future research.

Document Doi Bibtex

Title:Enhancing endoscopic image classification with symptom localization and data augmentation
Keywords:endoscopic image, symptoms localization, anatomical landmarks detection, object detection, image classification
Type:Conference
Language:English
City:Nice
Country:FRANCE
Date:
Department:Data Science
Eurecom ref:6083
Copyright: © ACM, 2019. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in MM 2019, 27th ACM International Conference on Multimedia, October 21-25, 2019, Nice, France http://dx.doi.org/10.1145/3343031.3356073
Bibtex: @inproceedings{EURECOM+6083, doi = {http://dx.doi.org/10.1145/3343031.3356073}, year = {2019}, title = {{E}nhancing endoscopic image classification with symptom localization and data augmentation}, author = {{H}oang, {T}rung-{H}ieu and {N}guyen, {H}ai-{D}ang and {N}guyen, {V}iet-{A}nh and {N}guyen, {T}hanh-{A}n and {N}guyen, {V}inh-{T}iep and {T}ran, {M}inh-{T}riet}, booktitle = {{MM} 2019, 27th {ACM} {I}nternational {C}onference on {M}ultimedia, {O}ctober 21-25, 2019, {N}ice, {F}rance}, address = {{N}ice, {FRANCE}}, month = {10}, url = {http://www.eurecom.fr/publication/6083} }