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Uncertainty-Aware Deep Learning for Segmenting Ultrasound Images of Breast Tumours

conference contribution
posted on 2024-04-26, 05:51 authored by AA Munia, Ibrahim HossainIbrahim Hossain, SM Jalali, P Tabarisaadi, A Rahman, S Nahavandi
Precise image segmentation is one of the dominant factors in disease diagnosis. A typical application is the segmentation of breast ultrasound images, allowing radiologists to suggest what to do next. After emerging deep learning technology especially convolutional neural networks (CNNs), the image segmentation model achieved state-of-the-art performance in various medical applications such as cancer detection and classification, lung node segmentation, cell segmentation and so on. However, despite these successes, a big question arises: to what extent is the model certain about the predicted result? Generally, most deep learning models focus on high accuracy but not on uncertainty of predicted results, which is not enough to make a critical real-life decision such as a disease diagnosis, where a wrong decision can be life-threatening. Hence for making a crucial decision, it is essential that the predicted result will provide not only accuracy but also estimate model uncertainty. Our contribution to this research is to build a system that predicts pixel-wise semantic segmentation and provides uncertainty estimation of the predicted results. It is achieved by adding a dropout layer during training and using Monte Carlo dropout in inference. We evaluate our model with the breast ultrasound image dataset (BUSI) and compare the results with a few other state-of-the-art methods where our method outperforms others in terms of IoU.

History

Volume

00

Pagination

4228-4235

Location

Honolulu, Oahu

Start date

2023-10-01

End date

2023-10-04

ISSN

1062-922X

ISBN-13

9798350337020

Language

eng

Publication classification

E1 Full written paper - refereed

Title of proceedings

SMC 2023 : Proceedings of the 2023 IEEE International Conference on Systems, Man and Cybernetics

Event

Systems, Man, and Cybernetics. Conference (2023 : Honolulu, Oahu)

Publisher

IEEE

Place of publication

Piscataway, N.J.

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