mlsanityData to 13 Oct 2026

Progressive Class-Wise Attention (PCA) Approach for Diagnosing Skin Lesions

arXiv 2306.07300 ↗First posted 11 Jun 2023cs.LG cs.AI cs.CVcited by 0

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Abstract (arXiv)

Skin cancer holds the highest incidence rate among all cancers globally. The importance of early detection cannot be overstated, as late-stage cases can be lethal. Classifying skin lesions, however, presents several challenges due to the many variations they can exhibit, such as differences in colour, shape, and size, significant variation within the same class, and notable similarities between different classes. This paper introduces a novel class-wise attention technique that equally regards each class while unearthing more specific details about skin lesions. This attention mechanism is progressively used to amalgamate discriminative feature details from multiple scales. The introduced technique demonstrated impressive performance, surpassing more than 15 cutting-edge methods including the winners of HAM1000 and ISIC 2019 leaderboards. It achieved an impressive accuracy rate of 97.40% on the HAM10000 dataset and 94.9% on the ISIC 2019 dataset.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 19 of its 70 references that are in the map. First posted on arXiv 11 Jun 2023.

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19 of 70 references.

References · 19

  1. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,713
  2. 2016Densely Connected Convolutional Networkscited by 6,707
  3. 2017Squeeze-and-Excitation Networkscited by 4,772
  4. 2018CBAM: Convolutional Block Attention Modulecited by 2,469
  5. 2020Designing Network Design Spacescited by 956
  6. 2018Reconciling modern machine learning practice and the bias-variance trade-offcited by 920
  7. 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
  8. 2017Residual Attention Network for Image Classificationcited by 386
  9. 2019BCN20000: Dermoscopic Lesions in the Wildcited by 225
  10. 2020Metrics for Multi-Class Classification: an Overviewcited by 142
  11. 2019A deep learning system for differential diagnosis of skin diseasescited by 118
  12. 2019A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classificationcited by 32
7 more
  1. 2023Retinal Vessel Segmentation via a Multi-resolution Contextual Network and Adversarial Learningcited by 19
  2. 2021Leveraging Image Complexity in Macro-Level Neural Network Design for Medical Image Segmentationcited by 16
  3. 2021Soft-Attention Improves Skin Cancer Classification Performancecited by 15
  4. 2019Skin Lesion Classification Using CNNs with Patch-Based Attention and Diagnosis-Guided Loss Weightingcited by 10
  5. 2017Automatic Skin Lesion Analysis using Large-scale Dermoscopy Images and Deep Residual Networkscited by 8
  6. 2018Skin Lesion Diagnosis using Ensembles, Unscaled Multi-Crop Evaluation and Loss Weightingcited by 6
  7. 2022Neural Network Compression by Joint Sparsity Promotion and Redundancy Reductioncited by 5

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