Multi-view contrastive learning for myelodysplastic syndrome screening : adding deep image representation to blood parameters

Myelodysplastic syndrome (MDS) is a clonal pathology affecting hematopoietic stem cells, leading to dysplasia and cytopenia on the complete blood count (CBC). MDS suspicion is established using the patient’s complete blood count (CBC) to count the number of different cell types, followed by a blood smear examination to detect any abnormal cells. While CBC analysis is widely automated nowadays, the blood smear examination process is still mainly realized manually by expert cytologists. In this work, we propose a deep learning framework leveraging multi-task learning and multi-view convolutional neural networks in order to extract deep representation of global cell dysplasia in patients. We then combine this deep latent space information of the network with CBC parameters to perform multimodal prediction of the MDS among patients. To train our framework, we gathered a multicentric dataset of patients labelled as MDS (n=60) or control (n=57), with both blood cells images and CBC parameters per sample. Our multimodal framework outperforms CBC-based, state-of-the-art methods for MDS diagnosis on this data with an accuracy of 79.55%.