# Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for the Diagnosis of Skin Lesions

arXiv: https://arxiv.org/abs/1703.01976 · HTML: https://mlsanity.com/paper/1703.01976/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2017.

## Built on by (most cited first, 8 of 8)

- [Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classification](https://mlsanity.com/paper/2102.01284/) (2021)
- [Projective Skip-Connections for Segmentation Along a Subset of Dimensions in Retinal OCT](https://mlsanity.com/paper/2108.00831/) (2021)
- [Boosting Medical Image Classification with Segmentation Foundation Model](https://mlsanity.com/paper/2406.11026/) (2024)
- [MT-TransUNet: Mediating Multi-Task Tokens in Transformers for Skin Lesion Segmentation and Classification](https://mlsanity.com/paper/2112.01767/) (2021)
- [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)
- [TopoImages: Incorporating Local Topology Encoding into Deep Learning Models for Medical Image Classification](https://mlsanity.com/paper/2508.01574/) (2025)
- [GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis](https://mlsanity.com/paper/2301.04410/) (2023)

## Abstract (arXiv)

This report describes our submission to the ISIC 2017 Challenge in Skin Lesion Analysis Towards Melanoma Detection. We have participated in the Part 3: Lesion Classification with a system for automatic diagnosis of nevus, melanoma and seborrheic keratosis. Our approach aims to incorporate the expert knowledge of dermatologists into the well known framework of Convolutional Neural Networks (CNN), which have shown impressive performance in many visual recognition tasks. In particular, we have designed several networks providing lesion area identification, lesion segmentation into structural patterns and final diagnosis of clinical cases. Furthermore, novel blocks for CNNs have been designed to integrate this information with the diagnosis processing pipeline.