27 datascEYEnce! Computer Vision News level label) instead of each single instance (segmentation mask). The approach makes it possible to generate a single activation map for each one of the nine AMD-related lesion types. Those masks then feed into global max-pooling which has one advantage over Grad-CAM approaches: it provides more intuitive explanation maps. Additionally to the lesion maps, the pipeline produces two more outputs: a vector revealing the presence or absence of a lesion and the final AMD diagnosis. In case you want to use their method for your future work, I collected some additional technical details of their setup worth mentioning: they used a VGG-16 backbone but according to José any backbone could be used since their method is model-agnostic. Here, it is important to exclude the last max-pooling layer from the backbone which would otherwise result in a too small activation map size. The adapted backbone then feeds into a 1x1 convolutional layer with nine output channels, one for each lesion. You can get more information about their workhere. I want to thank José again for the interview and wish him the best of luck for his ongoing PhD journey where he focuses on multimodal and self-supervised learning for retinal imaging! If you are interested in his work and are attending MICCAI 2023, I would recommend to keep an eye out for “Self-supervised learning via inter-modal reconstruction and feature projection networks for labelefficient 3D-to-2D segmentation”! Additionally, you can find a publicly available version here and a code implementation here!
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