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Automated Detection of Acute Promyelocytic Leukemia in Blood Films and Bone Marrow Aspirates with Annotation-free Deep Learning
While optical microscopy inspection of blood films and bone marrow aspirates by a hematologist is a crucial step in establishing diagnosis of acute leukemia, especially in low-resource settings where other diagnostic modalities might not be available, the task remains time-consuming and prone to human inconsistencies.
Autonomous object harvesting using synchronized optoelectronic microrobots
Optoelectronic tweezer-driven microrobots (OETdMs) are a versatile micromanipulation technology based on the use of light induced dielectrophoresis to move small dielectric structures (microrobots) across a photoconductive substrate.
Whole-Sample Mapping of Cancerous and Benign Tissue Properties
Structural and mechanical differences between cancerous and healthy tissue give rise to variations in macroscopic properties such as visual appearance and elastic modulus that show promise as signatures for early cancer detection.
Deep Learning Enhanced Extended Depth-of-Field for Thick Blood-Film Malaria High-Throughput Microscopy
Here we address the problem of Extended Depth-of-Field (EDoF) in thick blood film microscopy for rapid automated malaria diagnosis.
Data-Driven Malaria Prevalence Prediction in Large Densely-Populated Urban Holoendemic sub-Saharan West Africa: Harnessing Machine Learning Approaches and 22-years of Prospectively Collected Data
Our Locality-specific Elastic-Net based Malaria Prediction System (LEMPS) achieves good generalization performance, both in magnitude and direction of the prediction, when tasked to predict monthly prevalence on previously unseen validation data (MAE<=6x10-2, MSE<=7x10-3) within a range of (+0. 1 to -0. 05) error-tolerance which is relevant and usable for aiding decision-support in a holoendemic setting.
