@inproceedings{SophiaKucera, author = {Sophia, Strasser and Kucera, Markus}, title = {Artificial intelligence in safety-relevant embedded systems - on autonomous robotic surgery}, series = {2021 10th International Congress on Advanced Applied Informatics (IIAI-AAI), 11-16 July 2021, Niigata, Japan}, booktitle = {2021 10th International Congress on Advanced Applied Informatics (IIAI-AAI), 11-16 July 2021, Niigata, Japan}, publisher = {IEEE}, isbn = {978-1-6654-2420-2}, doi = {10.1109/IIAI-AAI53430.2021.00089}, pages = {506 -- 509}, abstract = {This paper focuses on Artificial Intelligence (AI) in robotic surgery. The question of safety and autonomy follows through the whole paper. Guidelines like Safety Integrity Levels which apply to dependable systems in general are described shortly. Overall, this work does not explicitly supply advantages of AI and instructional guidelines to build autonomous robots, instead, concentrates on challenges in the use of AI. In conclusion, there are still many open issues in the use of AI which cause potential gaps in reliability.}, language = {en} } @article{SouzaJrMendelStrasseretal., author = {Souza Jr., Luis Antonio de and Mendel, Robert and Strasser, Sophia and Ebigbo, Alanna and Probst, Andreas and Messmann, Helmut and Papa, Jo{\~a}o Paulo and Palm, Christoph}, title = {Convolutional Neural Networks for the evaluation of cancer in Barrett's esophagus: Explainable AI to lighten up the black-box}, series = {Computers in Biology and Medicine}, volume = {135}, journal = {Computers in Biology and Medicine}, publisher = {Elsevier}, issn = {0010-4825}, doi = {10.1016/j.compbiomed.2021.104578}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-20126}, pages = {1 -- 14}, abstract = {Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their level of accountability and transparency must be provided in such evaluations. The reliability related to machine learning predictions must be explained and interpreted, especially if diagnosis support is addressed. For this task, the black-box nature of deep learning techniques must be lightened up to transfer its promising results into clinical practice. Hence, we aim to investigate the use of explainable artificial intelligence techniques to quantitatively highlight discriminative regions during the classification of earlycancerous tissues in Barrett's esophagus-diagnosed patients. Four Convolutional Neural Network models (AlexNet, SqueezeNet, ResNet50, and VGG16) were analyzed using five different interpretation techniques (saliency, guided backpropagation, integrated gradients, input × gradients, and DeepLIFT) to compare their agreement with experts' previous annotations of cancerous tissue. We could show that saliency attributes match best with the manual experts' delineations. Moreover, there is moderate to high correlation between the sensitivity of a model and the human-and-computer agreement. The results also lightened that the higher the model's sensitivity, the stronger the correlation of human and computational segmentation agreement. We observed a relevant relation between computational learning and experts' insights, demonstrating how human knowledge may influence the correct computational learning.}, subject = {Deep Learning}, language = {en} }