Acute lymphoblastic leukemia cells image analysis with deep bagging ensemble learning

Liu, Ying; Long, Feixiao

doi:10.1101/580852

Cited by 20 publications

(16 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These two models are then fine-tuned jointly on whole training dataset to obtain the final predictions. These methods are able to achieve a weighted F 1 score of 87.6% (Liu & Long, 2019), and 88.9% (Prellberg & Kramer, 2019) as compared to 94.8% of the proposed method. Apart from these two approaches, other participants have used methodologies based on VGGNet, AlexNet, InceptionV3, DenseNet and MobileNet which have performed below the challenge winner's best performance of 91%.…”

Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 91%

“…This C-NMC 2019 dataset has recently been used by Prellberg & Kramer (2019) and Liu & Long (2019). Prellberg & Kramer (2019) have used training set to fine-tune a pretrained SE-ResNeXt50 architecture.…”

Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 99%

“…Prellberg & Kramer (2019) have used training set to fine-tune a pretrained SE-ResNeXt50 architecture. Liu & Long (2019) have divided the larger class samples into two subsets, which are then combined with other class samples to train two different models initialized with pretrained Inception ResNets weights. These two models are then fine-tuned jointly on whole training dataset to obtain the final predictions.…”

Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 99%

See 2 more Smart Citations

SDCT-AuxNet : DCT augmented stain deconvolutional CNN with auxiliary classifier for cancer diagnosis

Gehlot

Gupta

2020

Medical Image Analysis

View full text Add to dashboard Cite

Acute lymphoblastic leukemia (ALL) is a pervasive pediatric white blood cell cancer across the globe. With the popularity of convolutional neural networks (CNNs), computer-aided diagnosis of cancer has attracted considerable attention. Such tools are easily deployable and are cost-effective. Hence, these can enable extensive coverage of cancer diagnostic facilities. However, the development of such a tool for ALL cancer was challenging so far due to the non-availability of a large training dataset. The visual similarity between the malignant and normal cells adds to the complexity of the problem. This paper discusses the recent release of a large dataset and presents a novel deep learning architecture for the classification of cell images of ALL cancer. The proposed architecture, namely, SDCT-AuxNet θ is a 2-module framework that utilizes a compact CNN as the main classifier in one module and a Kernel SVM as the auxiliary classifier in the other one. While CNN classifier uses features through bilinear-pooling, spectral-averaged features are used by the auxiliary classifier. Further, this CNN is trained on the stain deconvolved quantity images in the optical density domain instead of the conventional RGB images. A novel test strategy is proposed that exploits both the classifiers for decision making using the confidence scores of their predicted class labels. Elaborate experiments have been carried out on our recently released public dataset of 15114 images of ALL cancer and healthy cells to establish the validity of the proposed methodology that is also robust to subject-level variability. A weighted F1 score of 94.8% is obtained that is best so far on this challenging dataset.

show abstract

Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 91%

Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 99%

Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 99%

See 1 more Smart Citation

SDCT-AuxNet : DCT augmented stain deconvolutional CNN with auxiliary classifier for cancer diagnosis

Gehlot

Gupta

2020

Medical Image Analysis

View full text Add to dashboard Cite

show abstract

“…Cell or cell nuclei segmentation is typically the first critical step for biomedical microscopy image analysis [1]. On the basis of accurate cell or cell nuclei segmentation, multiple biological or medical analysis can be performed subsequently, including cell type classification [2], particular cell counting [3], cell phenotype analysis [4] etc., providing valuable diagnostic information for doctors and researchers. Although conventional image processing techniques are still employed for this time and labor consuming task, they often cannot achieve the optimized performance due to multiple reasons, such as limited capability of dealing with diverse images [1].…”

Section: Introductionmentioning

confidence: 99%

Microscopy cell nuclei segmentation with enhanced U-Net

Long

2020

BMC Bioinformatics

Self Cite

View full text Add to dashboard Cite

Background: Cell nuclei segmentation is a fundamental task in microscopy image analysis, based on which multiple biological related analysis can be performed. Although deep learning (DL) based techniques have achieved state-of-the-art performances in image segmentation tasks, these methods are usually complex and require support of powerful computing resources. In addition, it is impractical to allocate advanced computing resources to each dark-or bright-field microscopy, which is widely employed in vast clinical institutions, considering the cost of medical exams. Thus, it is essential to develop accurate DL based segmentation algorithms working with resources-constraint computing. Results: An enhanced, light-weighted U-Net (called U-Net+) with modified encoded branch is proposed to potentially work with low-resources computing. Through strictly controlled experiments, the average IOU and precision of U-Net+ predictions are confirmed to outperform other prevalent competing methods with 1.0% to 3.0% gain on the first stage test set of 2018 Kaggle Data Science Bowl cell nuclei segmentation contest with shorter inference time. Conclusions: Our results preliminarily demonstrate the potential of proposed U-Net+ in correctly spotting microscopy cell nuclei with resources-constraint computing.

show abstract

“…Treatment for blood cancer depends on the age of the patient, type, how fast the cancer is progressing, infected areas, etc. [8]. The blood count is one of the prime factors for the distinction for categorizing the type of blood cancer.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of White Blood Cancer From Bone Marrow Microscopic Images Using Convolutional Neural Networks

et al. 2020

View full text Add to dashboard Cite

Leukocytes, produced in the bone marrow, make up around one percent of all blood cells. Uncontrolled growth of these white blood cells leads to the birth of blood cancer. Out of the three different types of cancers, the proposed study provides a robust mechanism for the classification of Acute Lymphoblastic Leukemia (ALL) and Multiple Myeloma (MM) using the SN-AM dataset. Acute lymphoblastic leukemia (ALL) is a type of cancer where the bone marrow forms too many lymphocytes. On the other hand, Multiple myeloma (MM), a different kind of cancer, causes cancer cells to accumulate in the bone marrow rather than releasing them into the bloodstream. Therefore, they crowd out and prevent the production of healthy blood cells. Conventionally, the process was carried out manually by a skilled professional in a considerable amount of time. The proposed model eradicates the probability of errors in the manual process by employing deep learning techniques, namely convolutional neural networks. The model, trained on cells' images, first pre-processes the images and extracts the best features. This is followed by training the model with the optimized Dense Convolutional neural network framework (termed DCNN here) and finally predicting the type of cancer present in the cells. The model was able to reproduce all the measurements correctly while it recollected the samples exactly 94 times out of 100. The overall accuracy was recorded to be 97.2%, which is better than the conventional machine learning methods like Support Vector Machine (SVMs), Decision Trees, Random Forests, Naive Bayes, etc. This study indicates that the DCNN model's performance is close to that of the established CNN architectures with far fewer parameters and computation time tested on the retrieved dataset. Thus, the model can be used effectively as a tool for determining the type of cancer in the bone marrow.

show abstract

Acute lymphoblastic leukemia cells image analysis with deep bagging ensemble learning

Cited by 20 publications

References 13 publications

SDCT-AuxNet : DCT augmented stain deconvolutional CNN with auxiliary classifier for cancer diagnosis

SDCT-AuxNet : DCT augmented stain deconvolutional CNN with auxiliary classifier for cancer diagnosis

Microscopy cell nuclei segmentation with enhanced U-Net

Automatic Detection of White Blood Cancer From Bone Marrow Microscopic Images Using Convolutional Neural Networks

Contact Info

Product

Resources

About