# Skin Lesion Classification Using Ensembles of Multi-Resolution EfficientNets with Meta Data

arXiv: https://arxiv.org/abs/1910.03910 · HTML: https://mlsanity.com/paper/1910.03910/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2019.

## Built on by (most cited first, 30 of 43)

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- [Review of multimodal machine learning approaches in healthcare](https://mlsanity.com/paper/2402.02460/) (2024)
- [Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classification](https://mlsanity.com/paper/2102.01284/) (2021)
- [FairDisCo: Fairer AI in Dermatology via Disentanglement Contrastive Learning](https://mlsanity.com/paper/2208.10013/) (2022)
- [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)
- [SSD-KD: A Self-supervised Diverse Knowledge Distillation Method for Lightweight Skin Lesion Classification Using Dermoscopic Images](https://mlsanity.com/paper/2203.11490/) (2022)
- [Out-of-Distribution Detection for Long-tailed and Fine-grained Skin Lesion Images](https://mlsanity.com/paper/2206.15186/) (2022)
- [Self-supervision for medical image classification: state-of-the-art performance with ~100 labeled training samples per class](https://mlsanity.com/paper/2304.05163/) (2023)
- [Graph-Based Intercategory and Intermodality Network for Multilabel Classification and Melanoma Diagnosis of Skin Lesions in Dermoscopy and Clinical Images](https://mlsanity.com/paper/2104.00201/) (2021)
- [Mapping industrial poultry operations at scale with deep learning and aerial imagery](https://mlsanity.com/paper/2112.10988/) (2021)
- [CASS: Cross Architectural Self-Supervision for Medical Image Analysis](https://mlsanity.com/paper/2206.04170/) (2022)
- [SkinDistilViT: Lightweight Vision Transformer for Skin Lesion Classification](https://mlsanity.com/paper/2308.08669/) (2023)
- [PatchAlign:Fair and Accurate Skin Disease Image Classification by Alignment with Clinical Labels](https://mlsanity.com/paper/2409.04975/) (2024)
- [Dermo-DOCTOR: A framework for concurrent skin lesion detection and recognition using a deep convolutional neural network with end-to-end dual encoders](https://mlsanity.com/paper/2102.01824/) (2021)
- [Semi-Supervised Federated Peer Learning for Skin Lesion Classification](https://mlsanity.com/paper/2103.03703/) (2021)
- [Deeply Supervised Skin Lesions Diagnosis with Stage and Branch Attention](https://mlsanity.com/paper/2205.04326/) (2022)
- [A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data](https://mlsanity.com/paper/2105.06141/) (2021)
- [Improved skin lesion recognition by a Self-Supervised Curricular Deep Learning approach](https://mlsanity.com/paper/2112.12086/) (2021)
- [Efficient Representation Learning for Healthcare with Cross-Architectural Self-Supervision](https://mlsanity.com/paper/2308.10064/) (2023)
- [Early Melanoma Diagnosis with Sequential Dermoscopic Images](https://mlsanity.com/paper/2110.05976/) (2021)
- [Siamese Neural Networks for Skin Cancer Classification and New Class Detection using Clinical and Dermoscopic Image Datasets](https://mlsanity.com/paper/2212.06130/) (2022)
- [Metadata Improves Segmentation Through Multitasking Elicitation](https://mlsanity.com/paper/2308.09411/) (2023)
- [Histopathological Image Classification and Vulnerability Analysis using Federated Learning](https://mlsanity.com/paper/2310.07380/) (2023)
- [Synthetic Data Generation for Long-Tail Medical Image Classification: A Case Study in Skin Lesions](https://mlsanity.com/paper/2605.03221/) (2026)
- [A Mechanism-Driven Theory of Phase Transitions in Active Learning](https://mlsanity.com/paper/2607.00144/) (2026)
- [Minimizing false negative rate in melanoma detection and providing insight into the causes of classification](https://mlsanity.com/paper/2102.09199/) (2021)
- [Bulk Production Augmentation Towards Explainable Melanoma Diagnosis](https://mlsanity.com/paper/2103.02198/) (2021)
- [Loss Estimators Improve Model Generalization](https://mlsanity.com/paper/2103.03788/) (2021)
- [Out of distribution detection for skin and malaria images](https://mlsanity.com/paper/2111.01505/) (2021)

## Abstract (arXiv)

In this paper, we describe our method for the ISIC 2019 Skin Lesion Classification Challenge. The challenge comes with two tasks. For task 1, skin lesions have to be classified based on dermoscopic images. For task 2, dermoscopic images and additional patient meta data have to be used. A diverse dataset of 25000 images was provided for training, containing images from eight classes. The final test set contains an additional, unknown class. We address this challenging problem with a simple, data driven approach by including external data with skin lesions types that are not present in the training set. Furthermore, multi-class skin lesion classification comes with the problem of severe class imbalance. We try to overcome this problem by using loss balancing. Also, the dataset contains images with very different resolutions. We take care of this property by considering different model input resolutions and different cropping strategies. To incorporate meta data such as age, anatomical site, and sex, we use an additional dense neural network and fuse its features with the CNN. We aggregate all our models with an ensembling strategy where we search for the optimal subset of models. Our best ensemble achieves a balanced accuracy of 74.2% using five-fold cross-validation. On the official test set our method is ranked first for both tasks with a balanced accuracy of 63.6% for task 1 and 63.4% for task 2.