Detection of subtype blood cells using deep learning

Tiwari, Prayag; Qian, Jia; Li, Qiuchi; Wang, Benyou; Gupta, Deepak; Khanna, Ashish; Rodrigues, Joel J. P. C.; Albuquerque, Victor Hugo C. de

doi:10.1016/j.cogsys.2018.08.022

Cited by 108 publications

(48 citation statements)

References 5 publications

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“…Hand-crafted cell nuclei boundary masks are also used as shape prior to filter the detection of CNNs [20]. Others applied CNNs for cell detection with pixel-level classification for each patch in the images [21]- [23]. Hofener et.…”

Section: B Deep Cell Detection Methodsmentioning

confidence: 99%

DETCIC: Detection of Elongated Touching Cells with Inhomogeneous Illumination using a Stack of Conditional Random Fields

Memariani

Nikou

Endres

et al. 2018

Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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Clostridioides difficile infection (C. diff) is the most common cause of death due to secondary infection in hospital patients in the United States. Detection of C. diff cells in scanning electron microscopy (SEM) images is an important task to quantify the efficacy of the under-development treatments. However, detecting C. diff cells in SEM images is a challenging problem due to the presence of inhomogeneous illumination and occlusion. An Illumination normalization pre-processing step destroys the texture and adds noise to the image. Furthermore, cells are often clustered together resulting in touching cells and occlusion. In this paper, DETCID, a deep cell detection method using adversarial training, specifically robust to inhomogeneous illumination and occlusion, is proposed. An adversarial network is developed to provide region proposals and pass the proposals to a feature extraction network. Furthermore, a modified IoU metric is developed to allow the detection of touching cells in various orientations. The results indicate that DETCID outperforms the state-of-the-art in detection of touching cells in SEM images by at least 20 percent improvement of mean average precision.

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Section: B Deep Cell Detection Methodsmentioning

confidence: 99%

DETCIC: Detection of Elongated Touching Cells with Inhomogeneous Illumination using a Stack of Conditional Random Fields

Memariani

Nikou

Endres

et al. 2018

Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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“…Compared to classic ML algorithms, CNN showed better results in terms of precision of identification of four types of WBCs, including eosinophils, neutrophils, lymphocytes, and monocytes. The precision was as high as 93% when the detection was limited to mononuclear cells versus polynuclear cells, and dropped to 88% when the four classes are considered [29]. However, to be suitable for real-time object detection, the ML software should be able to identify normal and abnormal leukocytes at a fast pace.…”

Section: Digital Microscopy For the Identification Of Normal And Abnomentioning

confidence: 99%

Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology

Achi

Khoury

2020

Cancers

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Digital Pathology is the process of converting histology glass slides to digital images using sophisticated computerized technology to facilitate acquisition, evaluation, storage, and portability of histologic information. By its nature, digitization of analog histology data renders it amenable to analysis using deep learning/artificial intelligence (DL/AI) techniques. The application of DL/AI to digital pathology data holds promise, even if the scope of use cases and regulatory framework for deploying such applications in the clinical environment remains in the early stages. Recent studies using whole-slide images and DL/AI to detect histologic abnormalities in general and cancer in particular have shown encouraging results. In this review, we focus on these emerging technologies intended for use in diagnostic hematology and the evaluation of lymphoproliferative diseases.

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“…Other authors (Shahin et al, 2019), who also use CNNs to classify rather than detect the five base leukocytes, show results that exceed the classical approach (accuracy: 96%), so it can be deduced that the separation between detection and classification is beneficial for the problem-solving. Most methods using CNNs require leukocytes to be already segmented/detected (Shahin et al, 2019;Choi et al, 2017;Jiang et al, 2018;Qin et al, 2018;Rehman et al, 2018 andTiwari et al, 2018). In this context, there are various approaches to CNNs: (e.g.)…”

Section: Introductionmentioning

confidence: 99%

Obtaining Deep Learning Models for Automatic Classification of Leukocytes

Rodrigues

Igrejas

Beato

2020

Machine Learning and Deep Learning in Real-Time Applications

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In this work, the authors classify leukocyte images using the neural network architectures that won the annual ILSVRC competition. The classification of leukocytes is made using pretrained networks and the same networks trained from scratch in order to select the ones that achieve the best performance for the intended task. The categories used are eosinophils, lymphocytes, monocytes, and neutrophils. The analysis of the results takes into account the amount of training required, the regularization techniques used, the training time, and the accuracy in image classification. The best classification results, on the order of 98%, suggest that it is possible, considering a competent preprocessing, to train a network like the DenseNet with 169 or 201 layers, in about 100 epochs, to classify leukocytes in microscopy images.

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Detection of subtype blood cells using deep learning

Cited by 108 publications

References 5 publications

DETCIC: Detection of Elongated Touching Cells with Inhomogeneous Illumination using a Stack of Conditional Random Fields

DETCIC: Detection of Elongated Touching Cells with Inhomogeneous Illumination using a Stack of Conditional Random Fields

Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology

Obtaining Deep Learning Models for Automatic Classification of Leukocytes

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