# Soft-Attention Improves Skin Cancer Classification Performance

arXiv: https://arxiv.org/abs/2105.03358 · HTML: https://mlsanity.com/paper/2105.03358/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 33 references that are in the map.
- First posted on arXiv 5 May 2021.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1710.05006/) (2017)
- [Residual Attention Network for Image Classification](https://mlsanity.com/paper/1704.06904/) (2017)
- [Skin Lesion Classification Using Ensembles of Multi-Resolution EfficientNets with Meta Data](https://mlsanity.com/paper/1910.03910/) (2019)
- [Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slides](https://mlsanity.com/paper/1811.08513/) (2018)
- [Data Augmentation for Skin Lesion Analysis](https://mlsanity.com/paper/1809.01442/) (2018)
- [Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classification](https://mlsanity.com/paper/2102.01284/) (2021)
- [Skin Lesion Synthesis with Generative Adversarial Networks](https://mlsanity.com/paper/1902.03253/) (2019)
- [Melanoma Detection using Adversarial Training and Deep Transfer Learning](https://mlsanity.com/paper/2004.06824/) (2020)
- [Dermatologist Level Dermoscopy Skin Cancer Classification Using Different Deep Learning Convolutional Neural Networks Algorithms](https://mlsanity.com/paper/1810.10348/) (2018)
- [Data, Depth, and Design: Learning Reliable Models for Skin Lesion Analysis](https://mlsanity.com/paper/1711.00441/) (2017)
- [Attention based Writer Independent Handwriting Verification](https://mlsanity.com/paper/2009.04532/) (2020)
- [Low-cost and high-performance data augmentation for deep-learning-based skin lesion classification](https://mlsanity.com/paper/2101.02353/) (2021)
- [Method to Classify Skin Lesions using Dermoscopic images](https://mlsanity.com/paper/2008.09418/) (2020)
- [Towards Automated Melanoma Screening: Proper Computer Vision & Reliable Results](https://mlsanity.com/paper/1604.04024/) (2016)

## Built on by (most cited first, 15 of 15)

- [A survey on attention mechanisms for medical applications: are we moving towards better algorithms?](https://mlsanity.com/paper/2204.12406/) (2022)
- [CosSIF: Cosine similarity-based image filtering to overcome low inter-class variation in synthetic medical image datasets](https://mlsanity.com/paper/2307.13842/) (2023)
- [BayTTA: Uncertainty-aware medical image classification with optimized test-time augmentation using Bayesian model averaging](https://mlsanity.com/paper/2406.17640/) (2024)
- [HDKD: Hybrid Data-Efficient Knowledge Distillation Network for Medical Image Classification](https://mlsanity.com/paper/2407.07516/) (2024)
- [Siamese Neural Networks for Skin Cancer Classification and New Class Detection using Clinical and Dermoscopic Image Datasets](https://mlsanity.com/paper/2212.06130/) (2022)
- [Med-IC: Fusing a Single Layer Involution with Convolutions for Enhanced Medical Image Classification and Segmentation](https://mlsanity.com/paper/2409.18506/) (2024)
- [A Distributed Hybrid Quantum Convolutional Neural Network for Medical Image Classification](https://mlsanity.com/paper/2501.06225/) (2025)
- [CQ CNN: A Hybrid Classical Quantum Convolutional Neural Network for Alzheimer's Disease Detection Using Diffusion Generated and U Net Segmented 3D MRI](https://mlsanity.com/paper/2503.02345/) (2025)
- [Adversarially Robust Medical Classification via Attentive Convolutional Neural Networks](https://mlsanity.com/paper/2210.14405/) (2022)
- [A Comprehensive Evaluation Study on Risk Level Classification of Melanoma by Computer Vision on ISIC 2016-2020 Datasets](https://mlsanity.com/paper/2302.09528/) (2023)
- [Progressive Class-Wise Attention (PCA) Approach for Diagnosing Skin Lesions](https://mlsanity.com/paper/2306.07300/) (2023)
- [Dynamic Perturbation-Adaptive Adversarial Training on Medical Image Classification](https://mlsanity.com/paper/2403.06798/) (2024)
- [Leveraging Spatial and Semantic Feature Extraction for Skin Cancer Diagnosis with Capsule Networks and Graph Neural Networks](https://mlsanity.com/paper/2403.12009/) (2024)
- [Demystifying Deep Learning-based Brain Tumor Segmentation with 3D UNets and Explainable AI (XAI): A Comparative Analysis](https://mlsanity.com/paper/2510.07785/) (2025)
- [Geometry-Aware Superpixel Graph Transformer with Metadata for Skin Lesion Classification](https://mlsanity.com/paper/2606.20390/) (2026)

## Abstract (arXiv)

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 toachieve 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, InceptionResNetv2 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.