Building Trust in Deep Learning System towards Automated Disease Detection


  • Zhan Wei Lim National University of Singapore
  • Mong Li Lee National University of Singapore
  • Wynne Hsu National University of Singapore
  • Tien Yin Wong National University of Singapore



Though deep learning systems have achieved high accuracy in detecting diseases from medical images, few such systems have been deployed in highly automated disease screening settings due to lack of trust in how well these systems can generalize to out-of-datasets. We propose to use uncertainty estimates of the deep learning system’s prediction to know when to accept or to disregard its prediction. We evaluate the effectiveness of using such estimates in a real-life application for the screening of diabetic retinopathy. We also generate visual explanation of the deep learning system to convey the pixels in the image that influences its decision. Together, these reveal the deep learning system’s competency and limits to the human, and in turn the human can know when to trust the deep learning system.




How to Cite

Lim, Z. W., Lee, M. L., Hsu, W., & Wong, T. Y. (2019). Building Trust in Deep Learning System towards Automated Disease Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9516-9521.



IAAI Technical Track: Emerging Papers