Browsing by Author "Chaware, Amey"

Now showing 1 - 3 of 3
  • Thumbnail Image
    ItemOpen Access
    A multiple instance learning approach for detecting COVID-19 in peripheral blood smears.
    (PLOS digital health, 2022-08) Cooke, Colin L; Kim, Kanghyun; Xu, Shiqi; Chaware, Amey; Yao, Xing; Yang, Xi; Neff, Jadee; Pittman, Patricia; McCall, Chad; Glass, Carolyn; Jiang, Xiaoyin Sara; Horstmeyer, Roarke
    A wide variety of diseases are commonly diagnosed via the visual examination of cell morphology within a peripheral blood smear. For certain diseases, such as COVID-19, morphological impact across the multitude of blood cell types is still poorly understood. In this paper, we present a multiple instance learning-based approach to aggregate high-resolution morphological information across many blood cells and cell types to automatically diagnose disease at a per-patient level. We integrated image and diagnostic information from across 236 patients to demonstrate not only that there is a significant link between blood and a patient's COVID-19 infection status, but also that novel machine learning approaches offer a powerful and scalable means to analyze peripheral blood smears. Our results both backup and enhance hematological findings relating blood cell morphology to COVID-19, and offer a high diagnostic efficacy; with a 79% accuracy and a ROC-AUC of 0.90.
  • Thumbnail Image
    ItemOpen Access
    Learned sensing: jointly optimized microscope hardware for accurate image classification.
    (Biomedical optics express, 2019-12) Muthumbi, Alex; Chaware, Amey; Kim, Kanghyun; Zhou, Kevin C; Konda, Pavan Chandra; Chen, Richard; Judkewitz, Benjamin; Erdmann, Andreas; Kappes, Barbara; Horstmeyer, Roarke
    Since its invention, the microscope has been optimized for interpretation by a human observer. With the recent development of deep learning algorithms for automated image analysis, there is now a clear need to re-design the microscope's hardware for specific interpretation tasks. To increase the speed and accuracy of automated image classification, this work presents a method to co-optimize how a sample is illuminated in a microscope, along with a pipeline to automatically classify the resulting image, using a deep neural network. By adding a "physical layer" to a deep classification network, we are able to jointly optimize for specific illumination patterns that highlight the most important sample features for the particular learning task at hand, which may not be obvious under standard illumination. We demonstrate how our learned sensing approach for illumination design can automatically identify malaria-infected cells with up to 5-10% greater accuracy than standard and alternative microscope lighting designs. We show that this joint hardware-software design procedure generalizes to offer accurate diagnoses for two different blood smear types, and experimentally show how our new procedure can translate across different experimental setups while maintaining high accuracy.
  • Thumbnail Image
    ItemOpen Access
    Physics-enhanced machine learning for virtual fluorescence microscopy
    (CoRR, 2020-04-08) Cooke, Colin L; Kong, Fanjie; Chaware, Amey; Zhou, Kevin C; Kim, Kanghyun; Xu, Rong; Ando, D Michael; Yang, Samuel J; Konda, Pavan Chandra; Horstmeyer, Roarke
    This paper introduces a new method of data-driven microscope design for virtual fluorescence microscopy. Our results show that by including a model of illumination within the first layers of a deep convolutional neural network, it is possible to learn task-specific LED patterns that substantially improve the ability to infer fluorescence image information from unstained transmission microscopy images. We validated our method on two different experimental setups, with different magnifications and different sample types, to show a consistent improvement in performance as compared to conventional illumination methods. Additionally, to understand the importance of learned illumination on inference task, we varied the dynamic range of the fluorescent image targets (from one to seven bits), and showed that the margin of improvement for learned patterns increased with the information content of the target. This work demonstrates the power of programmable optical elements at enabling better machine learning algorithm performance and at providing physical insight into next generation of machine-controlled imaging systems.