Not logged in.
Quick Search - Contribution
Contribution Details
| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Explaining CNN-Based Active Tuberculosis Detection in Chest X-Rays through Saliency Mapping Techniques |
| Organization Unit | |
| Authors |
|
| Supervisors |
|
| Language |
|
| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2023 |
| Abstract Text | Tuberculosis (TB) is an infectious disease caused by the bacterium Mycobacterium tuberculosis, which is one of the leading causes of death worldwide. Various Deep Convolutional Neural Network models have gained popularity to help during the TB screening process by detecting patients with active Tuberculosis from their Chest X-Rays. To help with further advancing the research, a new publicly available dataset, TBX11K, has been used to increase the number of samples during training for existing replaceable state-of-the-art models. In the first step, the model’s performance was evaluated to see if an improvement through the addition of more TB-related data was observable. It was shown that state-of-the-art replicable binary classifier models could further be improved through the inclusion of more data. Further, there is a lack of focus on generating and evaluating explanations for such models. The preferred methods currently are saliency mapping techniques such as Grad-CAM, to generate visual explanations based on the model’s decision-making process, by overlaying heatmaps over the Chest X-Rays. The selected TBX11K dataset includes ground truth bounding box labels, which makes it possible to evaluate if the visualisations were correct. There are various evaluation metrics to evaluate the faithfulness and localisation performance of the saliency mapping techniques according to ground truth labels. Two of them have been identified to be useful, namely RemOve and Debias, and Proportional Energy. RemOve and Debias was used to observe if there is one universal saliency mapping technique that performs well for all models for the task of active Tuberculosis detection. Further, based on these two metrics, a new metric was proposed, ROAD-Normalised PropEng Average, to measure the overall best-performing model and Saliency Mapping Technique combination. From the evaluation with RemOve and Debias, it was concluded that there does not seem to be a universal saliency mapping technique that performs well on all model architectures for the de- tection of active Tuberculosis. Thus, it is recommended to always consider the underlying model before choosing the optimal saliency mapping technique. Further, through the use of the ROAD-Normalised PropEng Average, it was concluded that one model in combination with a saliency mapping technique offered the best trade-off between faithfulness and correctness of the visualisations. This was the multi-label DenseNet-121 model with Eigen-CAM. To obtain accurate clas- sifications of active Tuberculosis with explainable and correct visualisations, it is recommended to use this model and visualisation technique combination. |
| PDF File |
Download
|
| Export |
BibTeX
|