TY - JOUR AU - Lim, Zhan Wei AU - Lee, Mong Li AU - Hsu, Wynne AU - Wong, Tien Yin PY - 2019/07/17 Y2 - 2024/03/28 TI - Building Trust in Deep Learning System towards Automated Disease Detection JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 33 IS - 01 SE - IAAI Technical Track: Emerging Papers DO - 10.1609/aaai.v33i01.33019516 UR - https://ojs.aaai.org/index.php/AAAI/article/view/5009 SP - 9516-9521 AB - <p>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.</p> ER -