# Unsupervised Learning for Concept Detection in Medical Images: A Comparative Analysis
## 4 Conclusions  
This paper took unsupervised representation learning techniques from state-of-the-art methods, facing them against a more traditional bags of visual words approach. The methods were evaluated with the biomedical concept detection sub-task of the ImageCLEF 2017 caption prediction task. We tested the hypothesis that a powerful image descriptor can contribute to efficient concept detection with some level of certainty, without observing the original image. Results were presented for six different approaches, where two of them relied on visual keypoint extraction and description algorithms and the other two of them were based on generative adversarial networks. Overall, these methods significantly outperformed our previous participation and were comparable with other techniques in the challenge.  
As identified in previous work [33] and hereby ensured, it is possible to obtain more powerful representations with modern deep learning approaches, in contrast with previously popular computer vision methods, such as the SIFT bags of visual words.  
Deep neural networks are known to be computationally intensive to train, but recent breakthroughs and open technologies have made these methods more accessible and practical. The convergence of an auto-encoder is relatively easy to attain, as the reconstruction loss constitutes a single major objective that can be optimized to a plateau. This is not the case for GANs, the nature of which imposes two adversarial objectives, thus bringing additional concerns for training stability. In particular, the results obtained with the BiGAN suggest that the model would benefit from better training. GANs can empirically provide good results, but the additional complexity, the difficulty of convergence, and the possibility of mode collapse can significantly cripple their performance in representation learning. Nonetheless, these issues are already a high focus of attention at this time and will likely lead to substantial improvements in GAN design and training.  
It is also important that these approaches are augmented with non-visual information. In particular, a medical imaging archive should take advantage of the available data beyond pixel data. Future work will consider semi-supervised learning as a means of building more descriptive representations from known categories and other annotations. Subsequently, these representations are to be evaluated in a medical information retrieval scenario as well as with other data sets in the medical imaging domain.  
Conceptualization, E.P.; Data curation, E.P.; Investigation, E.P.; Methodology, E.P.; Software, E.P.; Supervision, C.C.; Validation, E.P.; Visualization, E.P.; Writing--original draft, E.P.; Writing--review & editing, C.C.  
This work was financed by the ERDF (European Regional Development Fund) through the Operational Programme for Competitiveness and Internationalisation-COMPETE 2020 Programme, and by National Funds through the FCT--Fundacao para a Ciencia e a Tecnologia, within project PTDC/EEI-ESS/6815/2014. [PERSON] is funded by the FCT under the grant PD/BD/105806/2014.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Type** & \(\mathbf{F_{1}}\) **Score** & **Precision** & **Recall** & **AUC** & \(\mathbf{k}\) & \(\mathbf{F_{1}}\) **Score (Test)** \\ \hline ORB & 0.043 & 0.030 & 0.106 & 0.552 & 4 & 0.0418 \\ SIFT & 0.060 & 0.043 & 0.134 & 0.567 & 3 & 0.0567 \\ SDAE & _0.080_ & 0.070 & 0.120 & 0.560 & 2 & **0.0751** \\ VAE & 0.036 & 0.025 & 0.087 & 0.543 & 4 & 0.0345 \\ BiGAN & 0.047 & 0.035 & 0.099 & 0.549 & 3 & 0.0473 \\ AAE & 0.072 & 0.063 & 0.109 & 0.554 & 2 & 0.0691 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The best \(F_{1}\) scores obtained from the vector similarity search for each representation learned with the highest scores shown in bold.