Visual In-Context Learning for Few-Shot Eczema Segmentation
Neelesh Kumar Oya Aran Venugopal Vasudevan
Automated diagnosis of eczema from digital camera images is crucial for
developing applications that allow patients to self-monitor their recovery. An
important component of this is the segmentation of eczema region from such
images. Current methods for eczema segmentation rely on deep neural networks
such as convolutional (CNN)-based U-Net or transformer-based Swin U-Net. While
effective, these methods require high volume of annotated data, which can be
difficult to obtain. Here, we investigate the capabilities of visual in-context
learning that can perform few-shot eczema segmentation with just a handful of
examples and without any need for retraining models. Specifically, we propose a
strategy for applying in-context learning for eczema segmentation with a
generalist vision model called SegGPT. When benchmarked on a dataset of
annotated eczema images, we show that SegGPT with just 2 representative example
images from the training dataset performs better (mIoU: 36.69) than a CNN U-Net
trained on 428 images (mIoU: 32.60). We also discover that using more number of
examples for SegGPT may in fact be harmful to its performance. Our result
highlights the importance of visual in-context learning in developing faster
and better solutions to skin imaging tasks. Our result also paves the way for
developing inclusive solutions that can cater to minorities in the demographics
who are typically heavily under-represented in the training data.