Evaluating object detector ensembles for improving the robustness of artifact detection in endoscopic video streams

06/15/2022
by   Pedro Esteban Chavarrias-Solano, et al.
0

In this contribution we use an ensemble deep-learning method for combining the prediction of two individual one-stage detectors (i.e., YOLOv4 and Yolact) with the aim to detect artefacts in endoscopic images. This ensemble strategy enabled us to improve the robustness of the individual models without harming their real-time computation capabilities. We demonstrated the effectiveness of our approach by training and testing the two individual models and various ensemble configurations on the "Endoscopic Artifact Detection Challenge" dataset. Extensive experiments show the superiority, in terms of mean average precision, of the ensemble approach over the individual models and previous works in the state of the art.

READ FULL TEXT

page 3

page 4

04/21/2020

Certifying Joint Adversarial Robustness for Model Ensembles

Deep Neural Networks (DNNs) are often vulnerable to adversarial examples...
04/11/2022

Ensemble learning using individual neonatal data for seizure detection

Sharing medical data between institutions is difficult in practice due t...
11/28/2020

Voting based ensemble improves robustness of defensive models

Developing robust models against adversarial perturbations has been an a...
02/11/2022

Towards Adversarially Robust Deepfake Detection: An Ensemble Approach

Detecting deepfakes is an important problem, but recent work has shown t...
10/31/2019

Enhancing Certifiable Robustness via a Deep Model Ensemble

We propose an algorithm to enhance certified robustness of a deep model ...
03/01/2018

Learning Sparse Structured Ensembles with SG-MCMC and Network Pruning

An ensemble of neural networks is known to be more robust and accurate t...
07/09/2019

Multiscale Visual Drilldown for the Analysis of Large Ensembles of Multi-Body Protein Complexes

When studying multi-body protein complexes, biochemists use computationa...