# Data-Driven Color Augmentation Techniques for Deep Skin Image Analysis

arXiv: https://arxiv.org/abs/1703.03702 · HTML: https://mlsanity.com/paper/1703.03702/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2017.

## Built on by (most cited first, 13 of 13)

- [Model-Agnostic Augmentation for Accurate Graph Classification](https://mlsanity.com/paper/2202.10107/) (2022)
- [AugDMC: Data Augmentation Guided Deep Multiple Clustering](https://mlsanity.com/paper/2306.13023/) (2023)
- [Planckian Jitter: countering the color-crippling effects of color jitter on self-supervised training](https://mlsanity.com/paper/2202.07993/) (2022)
- [Boosting Medical Image Classification with Segmentation Foundation Model](https://mlsanity.com/paper/2406.11026/) (2024)
- [Fine-Grained Self-Supervised Learning with Jigsaw Puzzles for Medical Image Classification](https://mlsanity.com/paper/2308.05770/) (2023)
- [Data-Driven Deep Supervision for Skin Lesion Classification](https://mlsanity.com/paper/2209.01527/) (2022)
- [Data efficient deep learning for medical image analysis: A survey](https://mlsanity.com/paper/2310.06557/) (2023)
- [Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents](https://mlsanity.com/paper/2412.11484/) (2024)
- [TopoImages: Incorporating Local Topology Encoding into Deep Learning Models for Medical Image Classification](https://mlsanity.com/paper/2508.01574/) (2025)
- [An augmentation strategy to mimic multi-scanner variability in MRI](https://mlsanity.com/paper/2103.12595/) (2021)
- [Analyzing the effectiveness of image augmentations for face recognition from limited data](https://mlsanity.com/paper/2105.08796/) (2021)
- [Adaptive Low-Rank Regularization with Damping Sequences to Restrict Lazy Weights in Deep Networks](https://mlsanity.com/paper/2106.09677/) (2021)
- [LLM-Based Visual Explanation Evaluation Framework for Assessing the Explainability of Facial Skin Disease Classification Models](https://mlsanity.com/paper/2606.16794/) (2026)

## Abstract (arXiv)

Dermoscopic skin images are often obtained with different imaging devices, under varying acquisition conditions. In this work, instead of attempting to perform intensity and color normalization, we propose to leverage computational color constancy techniques to build an artificial data augmentation technique suitable for this kind of images. Specifically, we apply the shades of gray color constancy technique to color-normalize the entire training set of images, while retaining the estimated illuminants. We then draw one sample from the distribution of training set illuminants and apply it on the normalized image. We employ this technique for training two deep convolutional neural networks for the tasks of skin lesion segmentation and skin lesion classification, in the context of the ISIC 2017 challenge and without using any external dermatologic image set. Our results on the validation set are promising, and will be supplemented with extended results on the hidden test set when available.