mlsanityData to 7 Oct 2026

Soft-Attention Improves Skin Cancer Classification Performance

arXiv 2105.03358 ↗First posted 5 May 2021eess.IV cs.CV cs.LGcited by 15

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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.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 23 of its 33 references that are in the map. First posted on arXiv 5 May 2021.

Builds on

23 of 33 references.

References · 23

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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  10. 2017Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)cited by 549
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11 more
  1. 2018Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slidescited by 29
  2. 2018Data Augmentation for Skin Lesion Analysiscited by 27
  3. 2021Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classificationcited by 23
  4. 2019Skin Lesion Synthesis with Generative Adversarial Networkscited by 22
  5. 2020Melanoma Detection using Adversarial Training and Deep Transfer Learningcited by 17
  6. 2018Dermatologist Level Dermoscopy Skin Cancer Classification Using Different Deep Learning Convolutional Neural Networks Algorithmscited by 11
  7. 2017Data, Depth, and Design: Learning Reliable Models for Skin Lesion Analysiscited by 8
  8. 2020Attention based Writer Independent Handwriting Verificationcited by 4
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  11. 2016Towards Automated Melanoma Screening: Proper Computer Vision & Reliable Resultscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 3; 2024: 5; 2025: 3; 2026: 1.

15 papers.

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  1. 2022A survey on attention mechanisms for medical applications: are we moving towards better algorithms?cited by 15
  2. 2023CosSIF: Cosine similarity-based image filtering to overcome low inter-class variation in synthetic medical image datasetscited by 5
  3. 2024BayTTA: Uncertainty-aware medical image classification with optimized test-time augmentation using Bayesian model averagingcited by 5
  4. 2024HDKD: Hybrid Data-Efficient Knowledge Distillation Network for Medical Image Classificationcited by 3
  5. 2022Siamese Neural Networks for Skin Cancer Classification and New Class Detection using Clinical and Dermoscopic Image Datasetscited by 1
  6. 2024Med-IC: Fusing a Single Layer Involution with Convolutions for Enhanced Medical Image Classification and Segmentationcited by 1
  7. 2025A Distributed Hybrid Quantum Convolutional Neural Network for Medical Image Classificationcited by 1
  8. 2025CQ CNN: A Hybrid Classical Quantum Convolutional Neural Network for Alzheimer's Disease Detection Using Diffusion Generated and U Net Segmented 3D MRIcited by 1
  9. 2022Adversarially Robust Medical Classification via Attentive Convolutional Neural Networkscited by 0
  10. 2023A Comprehensive Evaluation Study on Risk Level Classification of Melanoma by Computer Vision on ISIC 2016-2020 Datasetscited by 0
  11. 2023Progressive Class-Wise Attention (PCA) Approach for Diagnosing Skin Lesionscited by 0
  12. 2024Dynamic Perturbation-Adaptive Adversarial Training on Medical Image Classificationcited by 0
3 more
  1. 2024Leveraging Spatial and Semantic Feature Extraction for Skin Cancer Diagnosis with Capsule Networks and Graph Neural Networkscited by 0
  2. 2025Demystifying Deep Learning-based Brain Tumor Segmentation with 3D UNets and Explainable AI (XAI): A Comparative Analysiscited by 0
  3. 2026Geometry-Aware Superpixel Graph Transformer with Metadata for Skin Lesion Classificationcited by 0