# Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classification

arXiv: https://arxiv.org/abs/2102.01284 · HTML: https://mlsanity.com/paper/2102.01284/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 80 references that are in the map.
- First posted on arXiv 2 Feb 2021.

## Builds on (most cited first, 30 of 39)

- [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)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [Designing Network Design Spaces](https://mlsanity.com/paper/2003.13678/) (2020)
- [Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1902.03368/) (2019)
- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [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)
- [BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition](https://mlsanity.com/paper/1912.02413/) (2019)
- [DropBlock: A regularization method for convolutional networks](https://mlsanity.com/paper/1810.12890/) (2018)
- [Fast AutoAugment](https://mlsanity.com/paper/1905.00397/) (2019)
- [BCN20000: Dermoscopic Lesions in the Wild](https://mlsanity.com/paper/1908.02288/) (2019)
- [Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning](https://mlsanity.com/paper/1602.03409/) (2016)
- [Maxout Networks](https://mlsanity.com/paper/1302.4389/) (2013)
- [Training Very Deep Networks](https://mlsanity.com/paper/1507.06228/) (2015)
- [A deep learning system for differential diagnosis of skin diseases](https://mlsanity.com/paper/1909.05382/) (2019)
- [Gradient Harmonized Single-stage Detector](https://mlsanity.com/paper/1811.05181/) (2018)
- [Data Augmentation using Random Image Cropping and Patching for Deep CNNs](https://mlsanity.com/paper/1811.09030/) (2018)
- [Skin Lesion Classification Using Ensembles of Multi-Resolution EfficientNets with Meta Data](https://mlsanity.com/paper/1910.03910/) (2019)
- [A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification](https://mlsanity.com/paper/1903.03313/) (2019)

## Built on by (most cited first, 23 of 23)

- [A survey, review, and future trends of skin lesion segmentation and classification](https://mlsanity.com/paper/2208.12232/) (2022)
- [Soft-Attention Improves Skin Cancer Classification Performance](https://mlsanity.com/paper/2105.03358/) (2021)
- [TFormer: A throughout fusion transformer for multi-modal skin lesion diagnosis](https://mlsanity.com/paper/2211.11393/) (2022)
- [ECL: Class-Enhancement Contrastive Learning for Long-tailed Skin Lesion Classification](https://mlsanity.com/paper/2307.04136/) (2023)
- [Towards Novel Class Discovery: A Study in Novel Skin Lesions Clustering](https://mlsanity.com/paper/2309.16451/) (2023)
- [FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoring](https://mlsanity.com/paper/2305.00738/) (2023)
- [Deeply Supervised Layer Selective Attention Network: Towards Label-Efficient Learning for Medical Image Classification](https://mlsanity.com/paper/2209.13844/) (2022)
- [Histopathological Image Classification and Vulnerability Analysis using Federated Learning](https://mlsanity.com/paper/2310.07380/) (2023)
- [Self-Supervised Multi-Modality Learning for Multi-Label Skin Lesion Classification](https://mlsanity.com/paper/2310.18583/) (2023)
- [Meta-Transfer Derm-Diagnosis: Exploring Few-Shot Learning and Transfer Learning for Skin Disease Classification in Long-Tail Distribution](https://mlsanity.com/paper/2404.16814/) (2024)
- [A Survey on Small Sample Imbalance Problem: Metrics, Feature Analysis, and Solutions](https://mlsanity.com/paper/2504.14800/) (2025)
- [Phased Progressive Learning with Coupling-Regulation-Imbalance Loss for Imbalanced Data Classification](https://mlsanity.com/paper/2205.12117/) (2022)
- [A Rotation Meanout Network with Invariance for Dermoscopy Image Classification and Retrieval](https://mlsanity.com/paper/2208.00627/) (2022)
- [MCPA: Multi-scale Cross Perceptron Attention Network for 2D Medical Image Segmentation](https://mlsanity.com/paper/2307.14588/) (2023)
- [Dynamic Perturbation-Adaptive Adversarial Training on Medical Image Classification](https://mlsanity.com/paper/2403.06798/) (2024)
- [Single-Shared Network with Prior-Inspired Loss for Parameter-Efficient Multi-Modal Imaging Skin Lesion Classification](https://mlsanity.com/paper/2403.19203/) (2024)
- [A Wavelet Guided Attention Module for Skin Cancer Classification with Gradient-based Feature Fusion](https://mlsanity.com/paper/2406.15128/) (2024)
- [DACB-Net: Dual Attention Guided Compact Bilinear Convolution Neural Network for Skin Disease Classification](https://mlsanity.com/paper/2407.03439/) (2024)
- [Non-parametric regularization for class imbalance federated medical image classification](https://mlsanity.com/paper/2407.12446/) (2024)
- [Towards Automated Differential Diagnosis of Skin Diseases Using Deep Learning and Imbalance-Aware Strategies](https://mlsanity.com/paper/2601.00286/) (2026)
- [Synthetic Melanoma Image Generation and Evaluation Using Generative Adversarial Networks](https://mlsanity.com/paper/2603.13497/) (2026)
- [Multimodal Skin Lesion Classification with Swin Transformer and Clinical Metadata Fusion](https://mlsanity.com/paper/2608.07574/) (2026)
- [Deep Learning for Biopsy-Free Subtyping of Basal Cell Carcinoma from Dermatoscopic Images](https://mlsanity.com/paper/2609.07180/) (2026)

## Abstract (arXiv)

Deep convolutional neural network (DCNN) models have been widely explored for skin disease diagnosis and some of them have achieved the diagnostic outcomes comparable or even superior to those of dermatologists. However, broad implementation of DCNN in skin disease detection is hindered by small size and data imbalance of the publically accessible skin lesion datasets. This paper proposes a novel single-model based strategy for classification of skin lesions on small and imbalanced datasets. First, various DCNNs are trained on different small and imbalanced datasets to verify that the models with moderate complexity outperform the larger models. Second, regularization DropOut and DropBlock are added to reduce overfitting and a Modified RandAugment augmentation strategy is proposed to deal with the defects of sample underrepresentation in the small dataset. Finally, a novel Multi-Weighted New Loss (MWNL) function and an end-to-end cumulative learning strategy (CLS) are introduced to overcome the challenge of uneven sample size and classification difficulty and to reduce the impact of abnormal samples on training. By combining Modified RandAugment, MWNL and CLS, our single DCNN model method achieved the classification accuracy comparable or superior to those of multiple ensembling models on different dermoscopic image datasets. Our study shows that this method is able to achieve a high classification performance at a low cost of computational resources and inference time, potentially suitable to implement in mobile devices for automated screening of skin lesions and many other malignancies in low resource settings.