We developed a fully automated deep learning system to grade prostate biopsies using 5759 biopsies from 1243 patients, and showed that this system achieved pathologist-level performance.
Prostate cancer is graded based on distinctive patterns in the tissue. At MIDL2018 I presented an unsupervised deep learning method, based on clustering adversarial autoencoders, to train a system to detect prostate cancer without using labeled data.
Building systems to detect tumor, in this case prostate cancer, is often hard due to a lack of data. Tumor annotations made by pathologists are often coarse due to time constraints. With this project we want to automatically refine these annotations by building a system that can automatically filter out irrelevant parts of the data.
Last week I gave a presentation at IoTDI 2016 regarding my Human SLAM research. My presentation can be viewed online.
A key problem (or challenge) within smart spaces is indoor localization: making estimates of users’ whereabouts. Without such information, systems are unable to react on the presence of users or, sometimes even more important, their absence. This can range from simply turning the lights on when someone enters a room to customizing the way devices interact with a specific user. Even more important for a system to know where users exactly are, is to know where users are relative to the devices it can control or use to sense the environment....