Deep modality association learning using histopathology images and immune cell sequencing data

Areej Alsaafin; Morteza Babaie; H. R. Tizhoosh

doi:10.1117/12.2651349

6 April 2023 Deep modality association learning using histopathology images and immune cell sequencing data

Areej Alsaafin, Morteza Babaie, H. R. Tizhoosh

Proceedings Volume 12471, Medical Imaging 2023: Digital and Computational Pathology; 124711C (2023) https://doi.org/10.1117/12.2651349
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Nowadays, the availability of different types of biomedical digital data offers many opportunities to investigate the relationships between the different modalities and thus develop a more comprehensive understanding of complex diseases such as cancer. In this paper, we propose a multi-modal model, called deep modality association learning (DMAL), that maps immune cell sequencing patterns to morphological tissue features of whole slide imageds (WSIs) in an embedding space. Useful information is extracted from T-cell receptor (TCR) sequences to guide the training process. DMAL maps the TCR features to the morphology features in histopathology images, which in turn enables the model to learn the association features between the two modalities. The discrimination power of the WSI-TCR association features has been assessed by classifying samples with different cancer subtypes. The conducted experiments have shown that DMAL generates more discriminative features compared to features obtained from single-modal data. In addition, DMAL has been utilized to predict TCR information from histopathology image representations without the need to have the actual TCR sequencing data.

Citation Download Citation

Areej Alsaafin, Morteza Babaie, and H. R. Tizhoosh "Deep modality association learning using histopathology images and immune cell sequencing data", Proc. SPIE 12471, Medical Imaging 2023: Digital and Computational Pathology, 124711C (6 April 2023); https://doi.org/10.1117/12.2651349

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