unprecedented accuracy. It boasts a relative WED error rate of 4%, well within the International Electrotechnical Commission (IEC) acceptance criteria of 10%. The primary motivation behind this work is to expedite the screening process and increase its efficiency, aiming to perform more screenings within the time available, ultimately making the procedure more accessible and affordable. “This is something that’s been in the works for some time now,” Brian tells us. “Now, we’re introducing it because we’ve reached a technological point where we can present it, but I’vebeen working on this project almost since I joined Siemens seven years ago. We started with all the smaller tasks, like guessing the patient’s internal anatomy. This was a brainstorming idea we had as a group together with our colleagues from the CT department of Siemens.” While the technology addressed most of the challenges, two significant hurdles had to be overcome: integrating the vast, non-aligned, and heterogeneous CT data from different sources into the training dataset and ensuring the model’s real-time accuracy during scans. Both challenges were effectively tackled through the incorporation of an AutoDecoder, eliminating the problem of convolutional networks taking different-sized images or sequences into inputs. 13 DAILY MICCAI Monday Brian Teixeira
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