Abstract
Deep learning algorithms have proven effective in solving many medical imaging tasks in recent years. The design of lightweight neural networks is gaining importance in the medical imaging community as not many hospitals and clinics are equipped with high computational resources to deploy large deep learning algorithms. Also, medical imaging data often comes with high class imbalance and thus there is a high necessity to develop deep learning models that can address this issue. With this motivation, a resource-efficient deep learning model called Lightweight-Fully Convolutional Network (LightFCN) is developed which can be deployed in clinical settings with limited computational resources. Label Distribution Aware Margin loss (LDAM) is used in the context of medical imaging for the first time for multi-label classification with class imbalance. The proposed model has a smaller memory footprint, a smaller number of parameters, lesser inference time and fewer Floating Point Operations (FLOPS) when compared to state-of-the-art models, without compromising on performance and can be deployed in clinical settings with limited computational resources. The model and the performance of the loss function are evaluated on the task of Intracranial Hemorrhage (ICH) classification on CT scans, and the model was deployed on a Raspberry Pi 4B (8 GB), on which inference times were compared. It is found that the proposed model significantly reduced the number of model parameters by a factor of 26, and reduced the inference time by a factor of 3, when compared to the popular lightweight network MobileNetV2.
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References
Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/
Burduja, M., Ionescu, R.T., Verga, N.: Accurate and efficient intracranial hemorrhage detection and subtype classification in 3D CT scans with convolutional and long short-term memory neural networks. Sensors 20(19), 5611 (2020)
Cao, K., Wei, C., Gaidon, A., Arechiga, N., Ma, T.: Learning imbalanced datasets with label-distribution-aware margin loss. In: Advances in Neural Information Processing Systems 32 (2019)
Cui, Y., Jia, M., Lin, T.Y., Song, Y., Belongie, S.: Class-balanced loss based on effective number of samples. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9268–9277 (2019)
Flanders, A.E., et al.: Construction of a machine learning dataset through collaboration: the RSNA 2019 brain CT hemorrhage challenge. Radiol. Artif. Intell. 2(3), e190211 (2020)
Google: Tensorflow lite (2021). https://www.tensorflow.org/lite/
Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)
Johnson, J.M., Khoshgoftaar, T.M.: Survey on deep learning with class imbalance. J. Big Data 6(1), 1–54 (2019)
Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., Talwalkar, A.: Hyperband: a novel bandit-based approach to hyperparameter optimization. J. Mach. Learn. Res. 18(1), 6765–6816 (2017)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2999–3007 (2017)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Masud, M.: A light-weight convolutional neural network architecture for classification of COVID-19 chest X-ray images. Multimedia syst., 1–10 (2022)
O’Malley, T., Bursztein, E., Long, J., Chollet, F., Jin, H., Invernizzi, L., et al.: Kerastuner (2019). https://github.com/keras-team/keras-tuner
Peng, H., Gong, W., Beckmann, C.F., Vedaldi, A., Smith, S.M.: Accurate brain age prediction with lightweight deep neural networks. Med. Image Anal. 68, 101871 (2021)
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV 2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
Shuvo, M.B., Ahommed, R., Reza, S., Hashem, M.: CNL-UNet: a novel lightweight deep learning architecture for multimodal biomedical image segmentation with false output suppression. Biomed. Signal Process. Control 70, 102959 (2021)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Zhang, X., Zhou, X., Lin, M., Sun, J.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848–6856 (2018)
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This work is supported by the start-up research grant given by the Science and Engineering Research Board (SERB), India.
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Lankireddy, P., Sindhura, C., Gorthi, S. (2022). A New Lightweight Architecture and a Class Imbalance Aware Loss Function for Multi-label Classification of Intracranial Hemorrhages. In: Lian, C., Cao, X., Rekik, I., Xu, X., Cui, Z. (eds) Machine Learning in Medical Imaging. MLMI 2022. Lecture Notes in Computer Science, vol 13583. Springer, Cham. https://doi.org/10.1007/978-3-031-21014-3_41
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