Graduate School and Research Center in Digital Sciences

An application of residual network and faster - RCNN for Medico: Multimedia task at MediaEval 2018

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

MEDIAEVAL 2018, MediaEval Benchmarking Initiative for Multimedia Evaluation Workshop, 29-31 October 2018, Sophia-Antipolis, France

The Medico: Multimedia Task focuses on developing an efficient framework for predicting and classifying abnormalities in endoscopic images of gastrointestinal (GI) tract. We present the HCMUS Team's approach, which employs a combination of Residual Neural Network and Faster R - CNN model to classify endoscopic images. We submit multiple runs with different modifications of the parameters in our combined model. Our methods show potential results through experiments.

Document Bibtex

Title:An application of residual network and faster - RCNN for Medico: Multimedia task at MediaEval 2018
Type:Conference
Language:English
City:Sophia-Antipolis
Country:FRANCE
Date:
Department:Data Science
Eurecom ref:5759
Copyright: CEUR
Bibtex: @inproceedings{EURECOM+5759, year = {2018}, title = {{A}n application of residual network and faster - {RCNN} for {M}edico: {M}ultimedia task at {M}edia{E}val 2018}, author = {{H}oang, {T}rung-{H}ieu and {N}guyen, {H}ai-{D}ang and {N}guyen, {T}hanh-{A}n and {N}guyen, {V}inh-{T}iep and {T}ran, {M}inh-{T}riet}, booktitle = {{MEDIAEVAL} 2018, {M}edia{E}val {B}enchmarking {I}nitiative for {M}ultimedia {E}valuation {W}orkshop, 29-31 {O}ctober 2018, {S}ophia-{A}ntipolis, {F}rance}, address = {{S}ophia-{A}ntipolis, {FRANCE}}, month = {10}, url = {http://www.eurecom.fr/publication/5759} }