# Combining Image Features and Patient Metadata to Enhance Transfer Learning

arXiv: https://arxiv.org/abs/2110.05239 · HTML: https://mlsanity.com/paper/2110.05239/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 8 Oct 2021.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?](https://mlsanity.com/paper/1706.00712/) (2017)
- [Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning](https://mlsanity.com/paper/1602.03409/) (2016)
- [Improving Palliative Care with Deep Learning](https://mlsanity.com/paper/1711.06402/) (2017)
- [Mathematics of Deep Learning](https://mlsanity.com/paper/1712.04741/) (2017)
- [Deep Learning for Tumor Classification in Imaging Mass Spectrometry](https://mlsanity.com/paper/1705.01015/) (2017)
- [Deep Transfer Learning for Automated Diagnosis of Skin Lesions from Photographs](https://mlsanity.com/paper/2011.04475/) (2020)

## Built on by (most cited first, 2 of 2)

- [Enhanced Transfer Learning Through Medical Imaging and Patient Demographic Data Fusion](https://mlsanity.com/paper/2111.14388/) (2021)
- [Demographic-aware fine-grained visual recognition of pediatric wrist pathologies](https://mlsanity.com/paper/2507.12964/) (2025)

## Abstract (arXiv)

In this work, we compare the performance of six state-of-the-art deep neural networks in classification tasks when using only image features, to when these are combined with patient metadata. We utilise transfer learning from networks pretrained on ImageNet to extract image features from the ISIC HAM10000 dataset prior to classification. Using several classification performance metrics, we evaluate the effects of including metadata with the image features. Furthermore, we repeat our experiments with data augmentation. Our results show an overall enhancement in performance of each network as assessed by all metrics, only noting degradation in a vgg16 architecture. Our results indicate that this performance enhancement may be a general property of deep networks and should be explored in other areas. Moreover, these improvements come at a negligible additional cost in computation time, and therefore are a practical method for other applications.