Deep meta-learning for the selection of accurate ultrasound based breast mass classifier
This work addresses the challenge of improving breast cancer diagnosis in ultrasound imaging by enhancing classifier selection, though it is incremental as it builds on existing handcrafted feature methods.
The authors tackled the problem of selecting the optimal classifier for breast mass differentiation in ultrasound images by developing a deep meta-network that recommends shape- or texture-based classifiers based on image appearance, achieving an AUC of 0.95 and accuracy of 0.91.
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.