Orthogonal Ensemble Networks for Biomedical Image Segmentation

05/22/2021
by   Agostina J. Larrazabal, et al.
37

Despite the astonishing performance of deep-learning based approaches for visual tasks such as semantic segmentation, they are known to produce miscalibrated predictions, which could be harmful for critical decision-making processes. Ensemble learning has shown to not only boost the performance of individual models but also reduce their miscalibration by averaging independent predictions. In this scenario, model diversity has become a key factor, which facilitates individual models converging to different functional solutions. In this work, we introduce Orthogonal Ensemble Networks (OEN), a novel framework to explicitly enforce model diversity by means of orthogonal constraints. The proposed method is based on the hypothesis that inducing orthogonality among the constituents of the ensemble will increase the overall model diversity. We resort to a new pairwise orthogonality constraint which can be used to regularize a sequential ensemble training process, resulting on improved predictive performance and better calibrated model outputs. We benchmark the proposed framework in two challenging brain lesion segmentation tasks –brain tumor and white matter hyper-intensity segmentation in MR images. The experimental results show that our approach produces more robust and well-calibrated ensemble models and can deal with challenging tasks in the context of biomedical image segmentation.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/27/2019

Biomedical Image Segmentation by Retina-like Sequential Attention Mechanism Using Only A Few Training Images

In this paper we propose a novel deep learning-based algorithm for biome...
research
12/22/2021

Maximum Entropy on Erroneous Predictions (MEEP): Improving model calibration for medical image segmentation

Modern deep neural networks have achieved remarkable progress in medical...
research
12/10/2018

Global Deep Learning Methods for Multimodality Isointense Infant Brain Image Segmentation

An important step in early brain development study is to perform automat...
research
05/21/2019

Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation

Semantic segmentation is essentially important to biomedical image analy...
research
02/10/2020

Knowledge Distillation for Brain Tumor Segmentation

The segmentation of brain tumors in multimodal MRIs is one of the most c...
research
11/26/2021

Efficient Self-Ensemble Framework for Semantic Segmentation

Ensemble of predictions is known to perform better than individual predi...
research
11/01/2020

Convolution Neural Networks for Semantic Segmentation: Application to Small Datasets of Biomedical Images

This thesis studies how the segmentation results, produced by convolutio...

Please sign up or login with your details

Forgot password? Click here to reset