Deep ensembles in bioimage segmentation

12/24/2021
by   Loris Nanni, et al.
0

Semantic segmentation consists in classifying each pixel of an image by assigning it to a specific label chosen from a set of all the available ones. During the last few years, a lot of attention shifted to this kind of task. Many computer vision researchers tried to apply autoencoder structures to develop models that can learn the semantics of the image as well as a low-level representation of it. In an autoencoder architecture, given an input, an encoder computes a low dimensional representation of the input that is then used by a decoder to reconstruct the original data. In this work, we propose an ensemble of convolutional neural networks (CNNs). In ensemble methods, many different models are trained and then used for classification, the ensemble aggregates the outputs of the single classifiers. The approach leverages on differences of various classifiers to improve the performance of the whole system. Diversity among the single classifiers is enforced by using different loss functions. In particular, we present a new loss function that results from the combination of Dice and Structural Similarity Index. The proposed ensemble is implemented by combining different backbone networks using the DeepLabV3+ and HarDNet environment. The proposal is evaluated through an extensive empirical evaluation on two real-world scenarios: polyp and skin segmentation. All the code is available online at https://github.com/LorisNanni.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/23/2020

Multi-Plateau Ensemble for Endoscopic Artefact Segmentation and Detection

Endoscopic artefact detection challenge consists of 1) Artefact detectio...
research
12/07/2017

Per-Pixel Feedback for improving Semantic Segmentation

Semantic segmentation is the task of assigning a label to each pixel in ...
research
04/02/2021

Deep ensembles based on Stochastic Activation Selection for Polyp Segmentation

Semantic segmentation has a wide array of applications ranging from medi...
research
01/18/2021

HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS

We propose a new convolution neural network called HarDNet-MSEG for poly...
research
10/15/2019

DeepGCNs: Making GCNs Go as Deep as CNNs

Convolutional Neural Networks (CNNs) have been very successful at solvin...
research
09/23/2020

Learning Non-Unique Segmentation with Reward-Penalty Dice Loss

Semantic segmentation is one of the key problems in the field of compute...
research
03/03/2022

Automated Single-Label Patent Classification using Ensemble Classifiers

Many thousands of patent applications arrive at patent offices around th...

Please sign up or login with your details

Forgot password? Click here to reset