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Found 4 papers, 3 papers with code
Class Activation Map-based Weakly supervised Hemorrhage Segmentation using Resnet-LSTM in Non-Contrast Computed Tomography images
In this work, we propose a novel weakly supervised DL method for ICH segmentation on NCCT scans, using image-level binary classification labels, which are less time-consuming and labor-efficient when compared to the manual labeling of individual ICH lesions.
Constrained self-supervised method with temporal ensembling for fiber bundle detection on anatomic tracing data
Anatomic tracing data provides detailed information on brain circuitry essential for addressing some of the common errors in diffusion MRI tractography.
Challenges for machine learning in clinical translation of big data imaging studies
The combination of deep learning image analysis methods and large-scale imaging datasets offers many opportunities to imaging neuroscience and epidemiology.
Brain tumour segmentation using a triplanar ensemble of U-Nets
Our method achieved an evaluation score that was the equal 5th highest value (with our method ranking in 10th place) in the BraTS'20 challenge, with mean Dice values of 0. 81, 0. 89 and 0. 84 on ET, WT and TC regions respectively on the BraTS'20 unseen test dataset.
