Label‐Free Leukemia Monitoring by Computer Vision

Doan, Minh; Case, Marian; Mašić, Dino; Hennig, Holger; McQuin, Claire; Caicedo, Juan C.; Singh, Shantanu; Goodman, Allen; Summers, Huw D.; Jamieson, David; Delft, Frederik W. van; Filby, Andrew; Carpenter, Anne E.; Rees, Paul; Irving, Julie

doi:10.1002/cyto.a.23987

Cited by 50 publications

(49 citation statements)

References 33 publications

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“…The learning rate was reduced by a factor of 10 when the validation loss failed to improve for 10 consecutive epochs. The model was trained for a maximum of 512 epochs, although early stopping generally terminated training before 200 epochs when there is no improvement in the validation loss after 50 consecutive epochs, as detailed in Doan et al (61). Training and validation data were randomly undersampled per blood unit across cell types to create a balanced dataset.…”

Section: Methodsmentioning

confidence: 99%

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Objective assessment of stored blood quality by deep learning

Doan

Sebastian

Caicedo

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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Stored red blood cells (RBCs) are needed for life-saving blood transfusions, but they undergo continuous degradation. RBC storage lesions are often assessed by microscopic examination or biochemical and biophysical assays, which are complex, time-consuming, and destructive to fragile cells. Here we demonstrate the use of label-free imaging flow cytometry and deep learning to characterize RBC lesions. Using brightfield images, a trained neural network achieved 76.7% agreement with experts in classifying seven clinically relevant RBC morphologies associated with storage lesions, comparable to 82.5% agreement between different experts. Given that human observation and classification may not optimally discern RBC quality, we went further and eliminated subjective human annotation in the training step by training a weakly supervised neural network using only storage duration times. The feature space extracted by this network revealed a chronological progression of morphological changes that better predicted blood quality, as measured by physiological hemolytic assay readouts, than the conventional expert-assessed morphology classification system. With further training and clinical testing across multiple sites, protocols, and instruments, deep learning and label-free imaging flow cytometry might be used to routinely and objectively assess RBC storage lesions. This would automate a complex protocol, minimize laboratory sample handling and preparation, and reduce the impact of procedural errors and discrepancies between facilities and blood donors. The chronology-based machine-learning approach may also improve upon humans’ assessment of morphological changes in other biomedically important progressions, such as differentiation and metastasis.

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Section: Methodsmentioning

confidence: 99%

“…Protocols for image preprocessing and deep-learning training of the supervised classification are similar to our previously established label-free imaging flow cytometry machine vision framework ( 61 ). In brief, the input images were contrast-stretched channel-wise and resized to 48 × 48 pixels by cropping or padding.…”

Section: Methodsmentioning

confidence: 99%

Objective assessment of stored blood quality by deep learning

Doan

Sebastian

Caicedo

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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“…Instead of performing pixel‐wise virtual stain/label predictions, DL is also very effective in holistically capturing complex “hidden” image features for classification. This has found broad applications in augmenting the label‐free measurements and provide improved specificity and classify disease progression [44,45] and cancer screening [46–48], as well as detect cell types [49,50], cell states [44,51], stem cell lineage [52–54], and drug response [55]. For example, in [44], Eulenberg et al .…”

Section: Applications In Biomedical Opticsmentioning

confidence: 99%

Deep Learning in Biomedical Optics

et al. 2021

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This article reviews deep learning applications in biomedical optics with a particular emphasis on image formation. The review is organized by imaging domains within biomedical optics and includes microscopy, fluorescence lifetime imaging, in vivo microscopy, widefield endoscopy, optical coherence tomography, photoacoustic imaging, diffuse tomography, and functional optical brain imaging. For each of these domains, we summarize how deep learning has been applied and highlight methods by which deep learning can enable new capabilities for optics in medicine. Challenges and opportunities to improve translation and adoption of deep learning in biomedical optics are also summarized. Lasers Surg. Med. © 2021 Wiley Periodicals LLC

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“…In such cases pre-trained CNNs trained on a large number of natural images like ImageNet can be used [6] . Pre-trained CNNs were earlier used successfully in diagnosis of prostate cancer [7] , [8] , breast cancer [9] , brain diseases [10] , leukemia [11] , etc. to name a few.…”

Section: Introductionmentioning

confidence: 99%

Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier

Abraham¹,

Nair

2020

Biocybernetics and Biomedical Engineering

142

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Corona virus disease-2019 (COVID-19) is a pandemic caused by novel coronavirus. COVID-19 is spreading rapidly throughout the world. The gold standard for diagnosing COVID-19 is reverse transcription-polymerase chain reaction (RT-PCR) test. However, the facility for RT-PCR test is limited, which causes early diagnosis of the disease difficult. Easily available modalities like X-ray can be used to detect specific symptoms associated with COVID-19. Pre-trained convolutional neural networks are widely used for computer-aided detection of diseases from smaller datasets. This paper investigates the effectiveness of multi-CNN, a combination of several pre-trained CNNs, for the automated detection of COVID-19 from X-ray images. The method uses a combination of features extracted from multi-CNN with correlation based feature selection (CFS) technique and Bayesnet classifier for the prediction of COVID-19. The method was tested using two public datasets and achieved promising results on both the datasets. In the first dataset consisting of 453 COVID-19 images and 497 non-COVID images, the method achieved an AUC of 0.963 and an accuracy of 91.16%. In the second dataset consisting of 71 COVID-19 images and 7 non-COVID images, the method achieved an AUC of 0.911 and an accuracy of 97.44%. The experiments performed in this study proved the effectiveness of pre-trained multi-CNN over single CNN in the detection of COVID-19.

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Label‐Free Leukemia Monitoring by Computer Vision

Cited by 50 publications

References 33 publications

Objective assessment of stored blood quality by deep learning

Objective assessment of stored blood quality by deep learning

Deep Learning in Biomedical Optics

Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier

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