GCTI-SN: Geometry-inspired chemical and tissue invariant stain normalization of microscopic medical images

Gupta, Anubha; Duggal, Rahul; Gehlot, Shiv; Gupta, Ritu; Mangal, Anvit; Kumar, Lalit; Thakkar, Nisarg; Satpathy, Devprakash

doi:10.1016/j.media.2020.101788

Cited by 54 publications

(24 citation statements)

References 17 publications

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“…This study was mainly based on a dataset of classification of normal versus malignant cells in B-ALL white blood cancer microscopic images (ISBI 2019) provided by SBI-Lab [ 11 , 12 , 43 , 44 ], which is available to the public at [ 21 ]. The goal of this challenge was to develop a machine learning solution for distinguishing normal cells from leukemic blast (malignant cells) in microscopic images of blood smears.…”

Section: Resultsmentioning

confidence: 99%

“…All training and test images were stored in 24-bit RGB format, with a consistent size of 450 × 450 pixels. Illumination errors, inconsistent staining, and noises of the images were fixed with the methods provided by [ 11 , 12 ]. Regarding the proposed stain-normalization method, by using an SD-Layer, nine learnable parameters were included in two standard CNN models (AlexNet and Texture-CNN [ 45 ]).…”

Section: Resultsmentioning

confidence: 99%

“… Examples of images from the ISBI 2019 challenge: Normal B cells (top row), leukemic B-lymphoblast cells (bottom row). The dataset used in this study was provided by SBILab [ 10 ], and was stain-normalized as in [ 11 , 12 ]. …”

Section: Figurementioning

confidence: 99%

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An Aggregated-Based Deep Learning Method for Leukemic B-lymphoblast Classification

2020

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Leukemia is a cancer of blood cells in the bone marrow that affects both children and adolescents. The rapid growth of unusual lymphocyte cells leads to bone marrow failure, which may slow down the production of new blood cells, and hence increases patient morbidity and mortality. Age is a crucial clinical factor in leukemia diagnosis, since if leukemia is diagnosed in the early stages, it is highly curable. Incidence is increasing globally, as around 412,000 people worldwide are likely to be diagnosed with some type of leukemia, of which acute lymphoblastic leukemia accounts for approximately 12% of all leukemia cases worldwide. Thus, the reliable and accurate detection of normal and malignant cells is of major interest. Automatic detection with computer-aided diagnosis (CAD) models can assist medics, and can be beneficial for the early detection of leukemia. In this paper, a single center study, we aimed to build an aggregated deep learning model for Leukemic B-lymphoblast classification. To make a reliable and accurate deep learner, data augmentation techniques were applied to tackle the limited dataset size, and a transfer learning strategy was employed to accelerate the learning process, and further improve the performance of the proposed network. The results show that our proposed approach was able to fuse features extracted from the best deep learning models, and outperformed individual networks with a test accuracy of 96.58% in Leukemic B-lymphoblast diagnosis.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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An Aggregated-Based Deep Learning Method for Leukemic B-lymphoblast Classification

2020

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“…To remove the variations in illumination, a stain normalization process has been applied to the images. The normalization procedures applied to this dataset have been described in detail in [21][22][23][24][25].…”

Section: Datasetmentioning

confidence: 99%

Hybrid Inception v3 XGBoost Model for Acute Lymphoblastic Leukemia Classification

Ramaneswaran

Srinivasan

Vincent

et al. 2021

Computational and Mathematical Methods in Medicine

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Acute lymphoblastic leukemia (ALL) is the most common type of pediatric malignancy which accounts for 25% of all pediatric cancers. It is a life-threatening disease which if left untreated can cause death within a few weeks. Many computerized methods have been proposed for the detection of ALL from microscopic cell images. In this paper, we propose a hybrid Inception v3 XGBoost model for the classification of acute lymphoblastic leukemia (ALL) from microscopic white blood cell images. In the proposed model, Inception v3 acts as the image feature extractor and the XGBoost model acts as the classification head. Experiments indicate that the proposed model performs better than the other methods identified in literature. The proposed hybrid model achieves a weighted F1 score of 0.986. Through experiments, we demonstrate that using an XGBoost classification head instead of a softmax classification head improves classification performance for this dataset for several different CNN backbones (feature extractors). We also visualize the attention map of the features extracted by Inception v3 to interpret the features learnt by the proposed model.

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“…A lot of traditional color normalization methods are proposed for eliminate pathology image style difference caused by staining procedure, mainly including color deconvolution [13] , [14] , [15] , [16] , [17] , [18] or stain spectral matching [19] , [20] , color transfer [21] , [22] , histogram matching [23] , and some other methods [24] , [25] . Color deconvolution methods were widely studied in past years.…”

Section: Introductionmentioning

confidence: 99%

An unsupervised style normalization method for cytopathology images

Chen

Cheng

et al. 2021

Computational and Structural Biotechnology Journal

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GCTI-SN: Geometry-inspired chemical and tissue invariant stain normalization of microscopic medical images

Cited by 54 publications

References 17 publications

An Aggregated-Based Deep Learning Method for Leukemic B-lymphoblast Classification

An Aggregated-Based Deep Learning Method for Leukemic B-lymphoblast Classification

Hybrid Inception v3 XGBoost Model for Acute Lymphoblastic Leukemia Classification

An unsupervised style normalization method for cytopathology images

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