A primary challenge of high-throughput imaging flow cytometry (IFC) is to analyze the vast amount of imaging data, especially in applications where ground truth labels are unavailable or hard to obtain. We present an unsupervised deep embedding algorithm, the Deep Convolutional Autoencoder-based Clustering (DCAEC) model, to cluster label-free IFC images without any prior knowledge of input labels. The DCAEC model first encodes the input images into the latent representations and then clusters based on the latent representations. Using the DCAEC model, we achieve a balanced accuracy of 91.9% for human white blood cell (WBC) clustering and 97.9% for WBC/leukemia clustering using the 3D IFC images and 3D DCAEC model. Above all, although no human recognizable features can separate the clusters of cells with protein localization, we demonstrate the fused DCAEC model can achieve a cluster balanced accuracy of 85.3% from the label-free 2D transmission and 3D side scattering images. To reveal how the neural network recognizes features beyond human ability, we use the gradient-weighted class activation mapping method to discover the cluster-specific visual patterns automatically. Evaluation results show that the automatically identified salient image regions have strong cluster-specific visual patterns for different clusters, which we believe is a stride for the interpretable neural network for cell analysis with high-throughput IFCs.

Recent advances in nanomaterials have profoundly transformed the landscape of biomedical imaging and analytical detection. By leveraging unique physical, chemical, and optical properties at the nanoscale, these materials enable unprecedented sensitivity, resolution, and specificity in both in vitro and in vivo applications. An analysis of the nanomaterials topic collection in Web of Science reveals that although biomedical imaging is not the most dominant domain in the nanomaterials field, it is rapidly growing across many disciplines and applications, including super-resolution nanoscopy, spatial sequencing technology, and immunology. Based on citation network topology and high-level topic modeling, we highlight recent advances in signal enhancement, targeted specificity, and mechanical innovations across multiple modalities, including optical imaging, contrast MRI, cytometry, and spatial sequencing.