Latest updates from the AI for Oncology Lab.
Open-source Deep MRI Reconstruction software published
Our open-source Deep Image REConstruction Toolkit (DIRECT) has been accepted for publication in the Journal of Open Source Software (JOSS)! DIRECT stores multiple DL model baselines such as the Recurrent Variational Network, implemented in PyTorch for end-to-end Accelerated MRI Reconstruction tasks and allows for use with datasets such as the fastMRI and Calgary Campinas Datasets!
DeepSMILE published in Medical Image Analysis
In this work we use whole-slide image (WSI) compression and multiple instance learning to predict homologous recombination deficiency and microsatellite instability from breast cancer and colorectal cancer WSIs. Both these labels are closely related to a patient’s response to immune- and targeted therapies.
AI for Oncology present at SPIE Medical Imaging
The AI for Oncology Lab was present at SPIE Medical Imaging 2022 in San Diego, presenting a diversity of work. Shannon Doyle explored the use of self-supervised techniques for the detection of ducts in histopathological surgical specimens, Yoni Schirris presented a weak-label approach to predict the tumor-infiltrating lymphocytes score and George Yiasemis presented a deep learning-based accelerated MRI reconstruction algorithm.
KWF grant on predicting invasive recurrence for DCIS has been awarded
Our project proposal together with the groups of Jelle Wesseling and Lodewyk Wessels to predict invasive recurrence of DCIS with AI has been awarded by KWF.