Background:
Accurate segmentation of Breast Infrared Thermography is an important
step for early detection of breast pathological changes. Automatic segmentation of Breast Infrared
Thermography is a very challenging task, as it is difficult to find an accurate breast contour and extract
regions of interest from it. Although several semi-automatic methods have been proposed for
segmentation, their performance often depends on hand-crafted image features, as well as preprocessing
operations.
Objective:
In this work, an approach to automatic semantic segmentation of the Breast Infrared
Thermography is proposed based on end-to-end fully convolutional neural networks and without
any pre or post-processing.
Methods:
The lack of labeled Breast Infrared Thermography data limits the complete utilization of
fully convolutional neural networks. The proposed model overcomes this challenge by applying
data augmentation and two-tier transfer learning from bigger datasets combined with adaptive
multi-tier fine-tuning before training the fully convolutional neural networks model.
Results:
Experimental results show that the proposed approach achieves better segmentation results:
97.986% accuracy; 98.36% sensitivity and 97.61% specificity compared to hand-crafted
segmentation methods.
Conclusion:
This work provided an end-to-end automatic semantic segmentation of Breast Infrared
Thermography combined with fully convolutional networks, adaptive multi-tier fine-tuning and
transfer learning. Also, this work was able to deal with challenges in applying convolutional neural
networks on such data and achieving the state-of-the-art accuracy.
End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural Networks
