TY - GEN
T1 - Soft Attention Improves Skin Cancer Classification Performance
AU - Datta, Soumyya Kanti
AU - Shaikh, Mohammad Abuzar
AU - Srihari, Sargur N.
AU - Gao, Mingchen
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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 ).
AB - 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 ).
UR - https://www.scopus.com/pages/publications/85115854670
U2 - 10.1007/978-3-030-87444-5_2
DO - 10.1007/978-3-030-87444-5_2
M3 - Conference contribution
AN - SCOPUS:85115854670
SN - 9783030874438
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 13
EP - 23
BT - Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data - 4th International Workshop, iMIMIC 2021, and 1st International Workshop, TDA4MedicalData 2021, Held in Conjunction with MICCAI 2021, Proceedings
A2 - Reyes, Mauricio
A2 - Henriques Abreu, Pedro
A2 - Cardoso, Jaime
A2 - Hajij, Mustafa
A2 - Zamzmi, Ghada
A2 - Rahul, Paul
A2 - Thakur, Lokendra
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2020 and 1st International Workshop on Topological Data Analysis and Its Applications for Medical Data, TDA4MedicalData 2021 held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
Y2 - 27 September 2021 through 27 September 2021
ER -