TY - JOUR T1 - Channel Embedding for Informative Protein Identification from Highly Multiplexed Images JF - bioRxiv DO - 10.1101/2020.03.24.004085 SP - 2020.03.24.004085 AU - Salma Abdel Magid AU - Won-Dong Jang AU - Denis Schapiro AU - Donglai Wei AU - James Tompkin AU - Peter K. Sorger AU - Hanspeter Pfister Y1 - 2020/01/01 UR - http://biorxiv.org/content/early/2020/03/25/2020.03.24.004085.abstract N2 - Interest is growing rapidly in using deep learning to classify biomedical images, and interpreting these deep-learned models is necessary for life-critical decisions and scientific discovery. Effective interpretation techniques accelerate biomarker discovery and provide new insights into the etiology, diagnosis, and treatment of disease. Most interpretation techniques aim to discover spatially-salient regions within images, but few techniques consider imagery with multiple channels of information. For instance, highly multiplexed tumor and tissue images have 30-100 channels and require interpretation methods that work across many channels to provide deep molecular insights. We propose a novel channel embedding method that extracts features from each channel. We then use these features to train a classifier for prediction. Using this channel embedding, we apply an interpretation method to rank the most discriminative channels. To validate our approach, we conduct an ablation study on a synthetic dataset. Moreover, we demonstrate that our method aligns with biological findings on highly multiplexed images of breast cancer cells while outperforming baseline pipelines. ER -