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RWTH Aachen
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An Explainable and Fully Automated Localization and Semantic Segmentation Technique for Biomedical Imaging

Thesis type
  • Master
Student Nazmi Shafin
Status Finished
Submitted in 2019
Supervisor(s)
Advisor(s)

Timely and correct intervention with surgical procedures by physicians is the most critical factor in reducing the morbidity rate of patients with chronic brain ailments, in particular patients with brain Tumor. Deep learning techniques in computer vision is making rapid progress in diagnosing and thereby aiding the physician in taking timely appropriate steps, and thus it has the potential to save many lives. However, often the deep learning models are like a black box that merely generates the outcome without giving any reason or explanation. The field of Artificial Intelligence needs models that have a capability to include explainability of the decisions that it is making. The US Food and Drug Administration requires any clinical decision support software to explain the rationale or support for its decisions to enable the users to independently review the basis of their recommendations. To meet this requirement and gain trust from medical practitioners, medical deep-learning systems should provide explanations for their outputs. This thesis work is an endeavour in that direction of explainable artificial intelligence whereby the results are generated and supplemented with an explanation of the decision that can be interpreted with visualizations generated by localization and semantic segmentation techniques which will ultimately be of significant help in aiding physicians for treating critical ailments such as brain tumor.

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