SD-Layer: Stain Deconvolutional Layer for CNNs in Medical Microscopic Imaging

Duggal, Rahul; Gupta, Anubha; Gupta, Ritu; Mallick, Pramit

doi:10.1007/978-3-319-66179-7_50

Cited by 70 publications

(35 citation statements)

References 13 publications

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“…To overcome the limitation of using a single dataset and to broaden the scope of our work, we extended our study to a second, independent and more recent dataset, C-NMC [30][31][32] . This dataset was used for the B-ALL normal versus malignant cell classification challenge at IEEE ISBI-2019 and consists of a large number of labeled images of normal and malignant cells.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…This dataset was used for the B-ALL normal versus malignant cell classification challenge at IEEE ISBI-2019 and consists of a large number of labeled images of normal and malignant cells. The cell images were extracted from blood smear microscopy images after normalizing the stain, as described in [30][31][32] . The total size of the training dataset is 10,661 images from 76 subjects.…”

Section: Dataset Descriptionmentioning

confidence: 99%

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Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features

Sahlol

Kollmannsberger

Ewees

2020

Sci Rep

202

101

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White Blood Cell (WBC) Leukaemia is caused by excessive production of leukocytes in the bone marrow, and image-based detection of malignant WBCs is important for its detection. Convolutional Neural Networks (CNNs) present the current state-of-the-art for this type of image classification, but their computational cost for training and deployment can be high. We here present an improved hybrid approach for efficient classification of WBC Leukemia. We first extract features from WBC images using VGGNet, a powerful CNN architecture, pre-trained on ImageNet. The extracted features are then filtered using a statistically enhanced Salp Swarm Algorithm (SESSA). This bio-inspired optimization algorithm selects the most relevant features and removes highly correlated and noisy features. We applied the proposed approach to two public WBC Leukemia reference datasets and achieve both high accuracy and reduced computational complexity. The SESSA optimization selected only 1 K out of 25 K features extracted with VGGNet, while improving accuracy at the same time. The results are among the best achieved on these datasets and outperform several convolutional network models. We expect that the combination of CNN feature extraction and SESSA feature optimization could be useful for many other image classification tasks.

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Section: Dataset Descriptionmentioning

confidence: 99%

Section: Dataset Descriptionmentioning

confidence: 99%

Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features

Sahlol

Kollmannsberger

Ewees

2020

Sci Rep

202

101

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“…2.2) in order to get rid of most black background. Staining and illumination error were presented in the images due to image capture with real clinical situations although reduced by stain normalization techniques as shown in [3,5]. The initial dataset was then split into original training set (see Sec.…”

Section: Details Of Initial Datasetmentioning

confidence: 99%

Acute lymphoblastic leukemia cells image analysis with deep bagging ensemble learning

Liu¹,

Long²

2019

Preprint

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Acute lymphoblastic leukemia (ALL) is a blood cancer which leads 111,000 depth globally in 2015. Recently, diagnosing ALL often involves the microscopic image analysis with the help of deep learning (DL) techniques. However, as most medical related problems, deficiency training samples and minor visual difference between ALL and normal cells make the image analysis task quite challenging. Herein, an augmented image enhanced bagging ensemble learning with elaborately designed training subsets were proposed to tackle above challenges. The weighted F1-scores of preliminary test set and final test are 0.84 and 0.88 respectively employing our ensemble model predictions and ranked within top 10% in ISBI-2019 Classification of Normal vs. Malignant White Blood Cancer Cells contest. Our results preliminarily show the efficacy and accuracy of employing DL based techniques in ALL cells image analysis.

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“…However, state of the art color deconvolution methods are highly dependent on the choice of a reference image, which is a very subjective and stain dependent task. Some methods suggested to include a stain separation layer as part of the network architecture (Duggal et al [19]). However, they show that their method is highly dependent on filter initialization, which should follow stain basis vectors.…”

Section: Discussionmentioning

confidence: 99%

Generalising multistain immunohistochemistry tissue segmentation using end‐to‐end colour deconvolution deep neural networks

Lahiani

Gildenblat²,

Navab

et al. 2019

IET Image Processing

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A key challenge in cancer immunotherapy biomarker research is quantification of pattern changes in microscopic whole slide images of tumor biopsies. Different cell types tend to migrate into various tissue compartments and form variable distribution patterns. Drug development requires correlative analysis of various biomarkers in and between the tissue compartments. To enable that, tissue slides are manually annotated by expert pathologists. Manual annotation of tissue slides is a labor intensive, tedious and error-prone task. Automation of this annotation process can improve accuracy and consistency while reducing workload and cost in a way that will positively influence drug development efforts. In this paper we present a novel one-shot color deconvolution deep learning method to automatically segment and annotate digitized slide images with multiple stainings into compartments of tumor, healthy tissue, and necrosis. We address the task in the context of drug development where multiple stains, tissue and tumor types exist and look into solutions for generalizations over these image populations.

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SD-Layer: Stain Deconvolutional Layer for CNNs in Medical Microscopic Imaging

Cited by 70 publications

References 13 publications

Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features

Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features

Acute lymphoblastic leukemia cells image analysis with deep bagging ensemble learning

Generalising multistain immunohistochemistry tissue segmentation using end‐to‐end colour deconvolution deep neural networks

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