Report
Deep meta-learning for the selection of accurate ultrasound based breast mass classifier
| العنوان: | Deep meta-learning for the selection of accurate ultrasound based breast mass classifier |
|---|---|
| المؤلفون: | Byra, Michal, Karwat, Piotr, Ryzhankow, Ivan, Komorowski, Piotr, Klimonda, Ziemowit, Fura, Lukasz, Pawlowska, Anna, Zolek, Norbert, Litniewski, Jerzy |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science Physics (Other) |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Physics - Medical Physics |
| الوصف: | Standard classification methods based on handcrafted morphological and texture features have achieved good performance in breast mass differentiation in ultrasound (US). In comparison to deep neural networks, commonly perceived as "black-box" models, classical techniques are based on features that have well-understood medical and physical interpretation. However, classifiers based on morphological features commonly underperform in the presence of the shadowing artifact and ill-defined mass borders, while texture based classifiers may fail when the US image is too noisy. Therefore, in practice it would be beneficial to select the classification method based on the appearance of the particular US image. In this work, we develop a deep meta-network that can automatically process input breast mass US images and recommend whether to apply the shape or texture based classifier for the breast mass differentiation. Our preliminary results demonstrate that meta-learning techniques can be used to improve the performance of the standard classifiers based on handcrafted features. With the proposed meta-learning based approach, we achieved the area under the receiver operating characteristic curve of 0.95 and accuracy of 0.91. Comment: Work presented at the 2022 IEEE International Ultrasonics Symposium, submission #2078 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2211.01892 |
| رقم الانضمام: | edsarx.2211.01892 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |