no code implementations • 22 Jul 2022 • Pierre Raillard, Lorenzo Cristoni, Andrew Walden, Roberto Lazzari, Thomas Pulimood, Louis Grandjean, Claudia AM Gandini Wheeler-Kingshott, Yipeng Hu, Zachary MC Baum
This work focuses on leveraging 'off-the-shelf' pre-trained models as deep feature extractors for scoring disease severity with minimal training time.
no code implementations • 22 Jul 2022 • Zachary MC Baum, Yipeng Hu, Dean C Barratt
Such networks can be trained by aligning the training image pairs while simultaneously improving test-time optimization efficacy; tasks which were previously considered two independent training and optimization processes.
no code implementations • 22 Jul 2022 • Zachary MC Baum, Tamas Ungi, Christopher Schlenger, Yipeng Hu, Dean C Barratt
This makes FPT amenable to real-world medical imaging applications where ground-truth deformations may be infeasible to obtain, or in scenarios where only a varying degree of completeness in the point sets to be aligned is available.
no code implementations • 6 Aug 2021 • Harry Mason, Lorenzo Cristoni, Andrew Walden, Roberto Lazzari, Thomas Pulimood, Louis Grandjean, Claudia AM Gandini Wheeler-Kingshott, Yipeng Hu, Zachary MC Baum
Furthermore, we offer a possible explanation that correlates the segmentation performance to label consistency and data domain diversity in this point-of-care lung ultrasound application.
1 code implementation • 28 Jul 2021 • Liam F Chalcroft, Jiongqi Qu, Sophie A Martin, Iani JMB Gayo, Giulio V Minore, Imraj RD Singh, Shaheer U Saeed, Qianye Yang, Zachary MC Baum, Andre Altmann, Yipeng Hu
When developing deep neural networks for segmenting intraoperative ultrasound images, several practical issues are encountered frequently, such as the presence of ultrasound frames that do not contain regions of interest and the high variance in ground-truth labels.