This repository contains the code for my Master's Thesis (full text) which deals with augmenting deep learning models with the ability to provide uncertainty estimates along with their predictions. We evaluate several uncertainty measures on a landmark localization task using a dataset of X-ray cephalograms. A short summary of the work follows, please see the full text if you'd like more details.

Deep convolutional neural networks (CNNs) achieve super-human results in image analysis but their outputs lack reliable information about the uncertainty of their predictions which prevents their wide-spread adaptation in medicine.

In this work we:

• Propose and train a CNN for the task of automatic cephalometric landmark localization on skull X-rays. This is usually done by a dentist manually and is a time-consuming and tedious process.
• Design and implement uncertainty metrics accompanying the trained models which are able to provide estimates of how certain the network is of its predictions (i.e., how much we should trust the predicted landmark positions).

• CNN was trained on a dataset [1] of 200 X-ray cephalograms (annotated with 19 landmark positions).
• Training done via heatmap regression (annotated landmark location is used to create a 2D heatmap with a Gaussian spike at that location).

We proposed three models with corresponding uncertainty measures:

1. Baseline is a CNN based on U-Net [2]. It uses the value of the maximum heatmap activation as a measure of how uncertain the CNN is with its prediction (we assume lower values indicate higher uncertainty). 2. Ensemble is an ensemble model of 15 Baseline models based on the work of Lakshminarayanan et al. [3]. 3. MC-Dropout is a CNN based on U-Net but additionally contains dropout layers which randomly remove some weights from the network both during training and evaluation (making the CNN stochastic so that multiple evaluations of the same image do not produce the exact same output). Based on the research by Gal et al. [4].

For both Ensemble and MC-Dropout models the prediction mean is used as the final predicted landmark position and the prediction variance (of the ensemble members and Monte Carlo samples respectively) as the uncertainty measure. We assume that as variance increases so does the models’ uncertainty.

• Models achieve performance close to state-of-the-art on the studied landmark localization task.
• Uncertainty measures were able to reliably detect data unsuitable for automatic evaluation.

• [1] 2016, Wang et al.: A benchmark for comparison of dental radiography analysis algorithms
• [2] 2015, Ronneberger, O.; Fischer, P.; Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation
• [3] 2017, Lakshminarayanan et al.: Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
• [4] 2016, Gal, Y.; Ghahramani, Z.: Dropout As a Bayesian Approximation

Follow the prepare_dataset.ipynb notebook to download and preprocess the data.

Use the following scripts to evaluate the performance of the models on the landmark localization task.

To train 15 independent Baseline models to form an Ensmeble as described in the thesis run train_ensemble.sh. To generate predictions for all ensemble members and then evaluate the full Ensemble run eval_ensemble.sh. Once this is done and the predictions are generated you can also evaluate a single Baseline member by running eval_baseline.sh.

Run train_mc_dropout.sh to train an MC-Dropout model as described in the thesis. To first generate predictions on the test set using 15 samples and then evaluate the performance of the model run eval_mc_dropout.sh.