Abstract
In clinical applications, neural networks must focus on and highlight the most important parts of an input image. Soft-Attention mechanism enables a neural network to achieve this goal. This paper investigates the effectiveness of Soft-Attention in deep neural architectures. The central aim of Soft-Attention is to boost the value of important features and suppress the noise-inducing features. We compare the performance of VGG, ResNet, Inception ResNet v2 and DenseNet architectures with and without the Soft-Attention mechanism, while classifying skin lesions. The original network when coupled with Soft-Attention outperforms the baseline [16] by 4.7% while achieving a precision of 93.7% on HAM10000 dataset [25]. Additionally, Soft-Attention coupling improves the sensitivity score by 3.8% compared to baseline [31] and achieves 91.6% on ISIC-2017 dataset [2]. The code is publicly available at github (https://github.com/skrantidatta/Attention-based-Skin-Cancer-Classification).
S. K. Datta and M. A. Shaikh are equal contributors.
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Datta, S.K., Shaikh, M.A., Srihari, S.N., Gao, M. (2021). Soft Attention Improves Skin Cancer Classification Performance. In: Reyes, M., et al. Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data. IMIMIC TDA4MedicalData 2021 2021. Lecture Notes in Computer Science(), vol 12929. Springer, Cham. https://doi.org/10.1007/978-3-030-87444-5_2
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