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

arXiv: https://arxiv.org/abs/2306.07300 · HTML: https://mlsanity.com/paper/2306.07300/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 70 references that are in the map.
- First posted on arXiv 11 Jun 2023.

## Builds on (most cited first, 19 of 19)

- [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)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Designing Network Design Spaces](https://mlsanity.com/paper/2003.13678/) (2020)
- [Reconciling modern machine learning practice and the bias-variance trade-off](https://mlsanity.com/paper/1812.11118/) (2018)
- [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)
- [BCN20000: Dermoscopic Lesions in the Wild](https://mlsanity.com/paper/1908.02288/) (2019)
- [Metrics for Multi-Class Classification: an Overview](https://mlsanity.com/paper/2008.05756/) (2020)
- [A deep learning system for differential diagnosis of skin diseases](https://mlsanity.com/paper/1909.05382/) (2019)
- [A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification](https://mlsanity.com/paper/1903.03313/) (2019)
- [Retinal Vessel Segmentation via a Multi-resolution Contextual Network and Adversarial Learning](https://mlsanity.com/paper/2304.12856/) (2023)
- [Leveraging Image Complexity in Macro-Level Neural Network Design for Medical Image Segmentation](https://mlsanity.com/paper/2112.11065/) (2021)
- [Soft-Attention Improves Skin Cancer Classification Performance](https://mlsanity.com/paper/2105.03358/) (2021)
- [Skin Lesion Classification Using CNNs with Patch-Based Attention and Diagnosis-Guided Loss Weighting](https://mlsanity.com/paper/1905.02793/) (2019)
- [Automatic Skin Lesion Analysis using Large-scale Dermoscopy Images and Deep Residual Networks](https://mlsanity.com/paper/1703.04197/) (2017)
- [Skin Lesion Diagnosis using Ensembles, Unscaled Multi-Crop Evaluation and Loss Weighting](https://mlsanity.com/paper/1808.01694/) (2018)
- [Neural Network Compression by Joint Sparsity Promotion and Redundancy Reduction](https://mlsanity.com/paper/2210.07451/) (2022)

## 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.