4 citations · 4 across the 3 of their papers we have counts for
3 papers
Transcriptome-supervised classification of tissue morphology using deep learning
Axel Andersson, Gabriele Partel, Leslie Solorzano +1
Deep learning has proven to successfully learn variations in tissue and cell morphology. Training of such models typically relies on expensive manual annotations. Here we conjectur…
Cell segmentation of in situ transcriptomics data using signed graph partitioning
Axel Andersson, Andrea Behanova, Carolina Wählby +1
The locations of different mRNA molecules can be revealed by multiplexed in situ RNA detection. By assigning detected mRNA molecules to individual cells, it is possible to identify…
End-to-end Multiple Instance Learning with Gradient Accumulation
Axel Andersson, Nadezhda Koriakina, Nataša Sladoje +1
Being able to learn on weakly labeled data, and provide interpretability, are two of the main reasons why attention-based deep multiple instance learning (ABMIL) methods have becom…