Last April 8th-11th took place the IEEE International Symposium on Biomedical Imaging (ISBI), a scientific conference dedicated to mathematical, algorithmic, and computational aspects of biological and biomedical imaging, across all scales of observation. The member of piccolo team and Tecnalia Research & Innovation, Alfonso Medela, presented the paper “Few-shot learning in histopathological images: reducing the need of labelled data on biological datasets”.
The team has been working on a few-shot approach in parallel with the acquisition of the datasets. To overcome the problem of scarce data in new imaging modalities such as OCT and MPT, few-shot techniques provide a solution to create algorithms out of a small number of images. The results showed that by using the proposed method it is possible to beat classical transfer-learning approach when only few images per class are available. The results encouraged the team to continue working on the same track and as a result they derived a loss function that further optimises this process of learning on few data, which is currently available (https://arxiv.org/abs/1905.10675) along with the source code (https://git.code.tecnalia.com/comvis_public/piccolo/constellation_loss).