Metadata Improves Segmentation Through Multitasking Elicitation
This work addresses the challenge of leveraging metadata for better segmentation in biomedical imaging, though it appears incremental as it builds on existing models.
The paper tackled the problem of underutilizing metadata in biomedical image segmentation by incorporating it via a channel modulation mechanism in convolutional networks, resulting in improved segmentation performance with a lightweight implementation.
Metainformation is a common companion to biomedical images. However, this potentially powerful additional source of signal from image acquisition has had limited use in deep learning methods, for semantic segmentation in particular. Here, we incorporate metadata by employing a channel modulation mechanism in convolutional networks and study its effect on semantic segmentation tasks. We demonstrate that metadata as additional input to a convolutional network can improve segmentation results while being inexpensive in implementation as a nimble add-on to popular models. We hypothesize that this benefit of metadata can be attributed to facilitating multitask switching. This aspect of metadata-driven systems is explored and discussed in detail.