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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Ensembles of Convolutional Neural Networks and Transformers for Polyp Segmentation.

Loris Nanni¹, Carlo Fantozzi¹, Andrea Loreggia²

¹Department of Information Engineering, University of Padova, 35122 Padova, Italy.

Sensors (Basel, Switzerland)

|July 11, 2023

Summary

This study reviews deep ensemble learning for polyp segmentation, developing novel ensembles that outperform existing methods. Averaging intermediate masks significantly improves performance in medical image analysis.

Keywords:

computer vision convolutional neural networks ensemble polyp segmentation transformers

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Area of Science:

Computer Vision
Medical Image Analysis
Deep Learning

Background:

Semantic segmentation classifies each pixel in an image, crucial for tasks like medical diagnostics.
Accurate polyp segmentation aids in early disease detection, reducing potential health consequences.
Deep ensemble learning models offer a promising approach to enhance segmentation accuracy.

Conclusions:

Deep ensemble learning, particularly with diverse components and novel mask averaging, significantly advances polyp segmentation.
The developed ensembles represent a state-of-the-art solution for medical image segmentation tasks.
This work provides a strong foundation for future research in automated medical diagnostics using computer vision.