Deep learning (DL) has demonstrated outstanding performance in a variety of applications. With the assistance of DL, healthcare seeks to reduce labor costs and increase access to high-quality medical resources. To ensure the stability and robustness of DL applications in medicine, it is essential to estimate the uncertainty. In this thesis, the research focuses on generating an uncertainty-aware nuclei detection framework for digital pathology. A neural network (NN) with uncertainty estimation is implemented using a Bayesian deep learning method based on MC Dropout to evaluate and study the method's reliability. By evaluating and discussing the uncertainty in DL, it is possible to comprehend why it is essential to include a mechanism for measuring uncertainty. With the implementation of the framework, the results demonstrate that uncertainty-aware DL approaches enable doctors to minimize manual labeling tasks and make better decisions based on uncertainty in diagnosis and treatment. We evaluate the models in terms of both model performance and model calibration. The results demonstrate that our solution increases precision and f1 score by 15% and 11%, respectively. Using our method, the negative log likelihood (NLL) was reduced by 12% as well.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-489084 |
| Date | January 2022 |
| Creators | Zhang, Chuxin |
| Publisher | Uppsala universitet, Institutionen för informationsteknologi |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | IT ; 22 127 |
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