Ms. Areej Alsaafin Profile

Areej Alsaafin

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Proceedings Article | 7 April 2023 Poster + Paper

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

Areej Alsaafin, Morteza Babaie, H. Tizhoosh

Proceedings Volume 12471, 124711C (2023) https://doi.org/10.1117/12.2651349

KEYWORDS: Genomics, Pattern recognition, Artificial intelligence, Histopathology, Lung cancer, Breast cancer, Tissues, Machine learning, Deep learning, Cancer, Biological samples

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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.

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