984 research outputs found
GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network
We propose a novel convolutional neural network for lesion detection from
weak labels. Only a single, global label per image - the lesion count - is
needed for training. We train a regression network with a fully convolutional
architecture combined with a global pooling layer to aggregate the 3D output
into a scalar indicating the lesion count. When testing on unseen images, we
first run the network to estimate the number of lesions. Then we remove the
global pooling layer to compute localization maps of the size of the input
image. We evaluate the proposed network on the detection of enlarged
perivascular spaces in the basal ganglia in MRI. Our method achieves a
sensitivity of 62% with on average 1.5 false positives per image. Compared with
four other approaches based on intensity thresholding, saliency and class maps,
our method has a 20% higher sensitivity.Comment: Article published in MICCAI 2017. We corrected a few errors from the
first version: padding, loss, typos and update of the DOI numbe
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