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Explainability for regression CNN in fetal head circumference estimation from ultrasound images

Jing Zhang Caroline Petitjean 1, 2 Florian Yger 3 Samia Ainouz
1 QuantIF-LITIS - Equipe Quantification en Imagerie Fonctionnelle
LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
2 DocApp - LITIS - Equipe Apprentissage
LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
Abstract : The measurement of fetal head circumference (HC) is performed throughout the pregnancy to monitor fetus growth using ultra-sound (US) images. Recently, methods that directly predict biometric from images, instead of resorting to segmentation, have emerged. In our previous work, we have proposed such method, based on a regression con-volutional neural network (CNN). If deep learning methods are the gold standard in most image processing tasks, they are often considered as black boxes and fails to provide interpretable decisions. In this paper, we investigate various saliency maps methods, to leverage their ability at explaining the predicted value of the regression CNN. Since saliency maps methods have been developed for classification CNN mostly, we provide an interpretation for regression saliency maps, as well as an adaptation of a perturbation-based quantitative evaluation of explanations methods. Results obtained on a public dataset of ultrasound images show that some saliency maps indeed exhibit the head contour as the most relevant features to assess the head circumference and also that the map quality depends on the backbone architecture and whether the prediction error is low or high.
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Submitted on : Wednesday, October 7, 2020 - 2:14:47 PM
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Jing Zhang, Caroline Petitjean, Florian Yger, Samia Ainouz. Explainability for regression CNN in fetal head circumference estimation from ultrasound images. Workshop on Interpretability of Machine Intelligence in Medical Image Computing at MICCAI 2020, Oct 2020, Lima, Peru. ⟨10.1007/978-3-030-61166-8_8⟩. ⟨hal-02960164⟩

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