# Multi-Plateau Ensemble for Endoscopic Artefact Segmentation and Detection

Source: [Multi-Plateau Ensemble for Endoscopic Artefact Segmentation and Detection](<https://www.sota2.com/research/paper/68fa68471e209131cea1b07a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:33:00.328Z

Authors: Suyog Jadhav, Udbhav Bamba, Arnav Chavan, Rishabh Tiwari, Aryan Raj

Published: 2020-03-23T08:28:36.000Z

## Abstract

Endoscopic artefact detection challenge consists of 1) Artefact detection, 2) Semantic segmentation, and 3) Out-of-sample generalisation. For Semantic segmentation task, we propose a multi-plateau ensemble of FPN (Feature Pyramid Network) with EfficientNet as feature extractor/encoder. For Object detection task, we used a three model ensemble of RetinaNet with Resnet50 Backbone and FasterRCNN (FPN + DC5) with Resnext101 Backbone}. A PyTorch implementation to our approach to the problem is available at https://github.com/ubamba98/EAD2020.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2003.10129>)

[PDF](<https://arxiv.org/pdf/2003.10129.pdf>)