Improved Dominance Soft Set Based Decision Rules with Pruning for Leukemia Image Classification

Acute lymphoblastic leukemia is a well-known type of pediatric cancer that affects the blood and bone marrow. If left untreated, it ends in fatal conditions due to its proliferation into the circulation system and other indispensable organs. All over the world, leukemia primarily attacks youngsters and grown-ups. The early diagnosis of leukemia is essential for the recovery of patients, particularly in the case of children. Computational tools for medical image analysis, therefore, have significant use and become the focus of research in medical image processing. The particle swarm optimization algorithm (PSO) is employed to segment the nucleus in the leukemia image. The texture, shape, and color features are extracted from the nucleus. In this article, an improved dominance soft set-based decision rules with pruning (IDSSDRP) algorithm is proposed to predict the blast and non-blast cells of leukemia. This approach proceeds with three distinct phases: (i) improved dominance soft set-based attribute reduction using AND operation in multi-soft set theory, (ii) generation of decision rules using dominance soft set, and (iii) rule pruning. The efficiency of the proposed system is compared with other benchmark classification algorithms. The research outcomes demonstrate that the derived rules efficiently classify cancer and non-cancer cells. Classification metrics are applied along with receiver operating characteristic (ROC) curve analysis to evaluate the efficiency of the proposed framework.

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Targeting Kinase-activating Genetic Lesions to Improve Therapy of Pediatric Acute Lymphoblastic Leukemia

Current Medicinal Chemistry ◽

10.2174/0929867324666170727101932 ◽

2018 ◽

Vol 25 (24) ◽

pp. 2811-2825 ◽

Cited By ~ 2

Author(s):

Raffaella Franca ◽

Natasa K. Kuzelicki ◽

Claudio Sorio ◽

Eleonora Toffoletti ◽

Oksana Montecchini ◽

...

Keyword(s):

Acute Lymphoblastic Leukemia ◽

Fusion Gene ◽

Hematologic Malignancy ◽

Lymphoblastic Leukemia ◽

Point Of View ◽

Lymphoid Cells ◽

Gene Encoding ◽

Pediatric All ◽

Blast Cells ◽

Tk Inhibitors

Acute lymphoblastic leukemia (ALL) is the most common hematologic malignancy in children, characterized by an abnormal proliferation of immature lymphoid cells. Thanks to risk-adapted combination chemotherapy treatments currently used, survival at 5 years has reached 90%. ALL is a heterogeneous disease from a genetic point of view: patients’ lymphoblasts may harbor in fact several chromosomal alterations, some of which have prognostic and therapeutic value. Of particular importance is the translocation t(9;22)(q34;q11.2) that leads to the formation of the BCR-ABL1 fusion gene, encoding a constitutively active chimeric tyrosine kinase (TK): BCR-ABL1 that is present in ~3% of pediatric ALL patients with B-immunophenotype and is associated with a poor outcome. This type of ALL is potentially treatable with specific TK inhibitors, such as imatinib. Recent studies have demonstrated the existence of a subset of BCR-ABL1 like leukemias (~10-15% of Bimmunophenotype ALL), whose blast cells have a gene expression profile similar to that of BCR-ABL1 despite the absence of t(9;22)(q34;q11.2). The precise pathogenesis of BCR-ABL1 like ALL is still to be defined, but they are mainly characterized by the activation of constitutive signal transduction pathways due to chimeric TKs different from BCR-ABL1. BCR-ABL1 like ALL patients represent a group with unfavorable outcome and are not identified by current risk criteria. In this review, we will discuss the design of targeted therapy for patients with BCR-ABL1 like ALL, which could consider TK inhibitors, and discuss innovative approaches suitable to identify the presence of patient’s specific chimeric TK fusion genes, such as targeted locus amplification or proteomic biosensors.

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Privacy-preserving Collaborative Training for Medical Image Analysis Based on Multi-Blockchain

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207323666201022110616 ◽

2020 ◽

Vol 23 ◽

Author(s):

Wanlu Zhang ◽

Qigang Wang ◽

Mei Li

Keyword(s):

Medical Image ◽

Data Privacy ◽

Medical Image Analysis ◽

Auxiliary Information ◽

Training Process ◽

Private Data ◽

Medical Institutions ◽

Model Training ◽

Collaborative Training ◽

Similar Task

Background: As artificial intelligence and big data analysis develop rapidly, data privacy, especially patient medical data privacy, is getting more and more attention. Objective: To strengthen the protection of private data while ensuring the model training process, this article introduces a multi-Blockchain-based decentralized collaborative machine learning training method for medical image analysis. In this way, researchers from different medical institutions are able to collaborate to train models without exchanging sensitive patient data. Method: Partial parameter update method is applied to prevent indirect privacy leakage during model propagation. With the peer-to-peer communication in the multi-Blockchain system, a machine learning task can leverage auxiliary information from another similar task in another Blockchain. In addition, after the collaborative training process, personalized models of different medical institutions will be trained. Results: The experimental results show that our method achieves similar performance with the centralized model-training method by collecting data sets of all participants and prevents private data leakage at the same time. Transferring auxiliary information from similar task on another Blockchain has also been proven to effectively accelerate model convergence and improve model accuracy, especially in the scenario of absence of data. Personalization training process further improves model performance. Conclusion: Our approach can effectively help researchers from different organizations to achieve collaborative training without disclosing their private data.

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Combinatorial Double Auction Based Meta-scheduler for Medical Image Analysis Application in Grid Environment

Recent Advances in Computer Science and Communications ◽

10.2174/2213275911666190320161934 ◽

2020 ◽

Vol 13 (5) ◽

pp. 999-1007

Author(s):

Karthikeyan Periyasami ◽

Arul Xavier Viswanathan Mariammal ◽

Iwin Thanakumar Joseph ◽

Velliangiri Sarveshwaran

Keyword(s):

Image Analysis ◽

Medical Image ◽

Medical Image Analysis ◽

Grid Resource ◽

Resource Broker ◽

Analysis Application ◽

Local Grid ◽

Grid Resource Broker ◽

Global Grid ◽

Grid Site

Background: Medical image analysis application has complex resource requirement. Scheduling Medical image analysis application is the complex task to the grid resources. It is necessary to develop a new model to improve the breast cancer screening process. Proposed novel Meta scheduler algorithm allocate the image analyse applications to the local schedulers and local scheduler submit the job to the grid node which analyses the medical image and generates the result sent back to Meta scheduler. Meta schedulers are distinct from the local scheduler. Meta scheduler and local scheduler have the aim at resource allocation and management. Objective: The main objective of the CDAM meta-scheduler is to maximize the number of jobs accepted. Methods: In the beginning, the user sends jobs with the deadline to the global grid resource broker. Resource providers sent information about the available resources connected in the network at a fixed interval of time to the global grid resource broker, the information such as valuation of the resource and number of an available free resource. CDAM requests the global grid resource broker for available resources details and user jobs. After receiving the information from the global grid resource broker, it matches the job with the resources. CDAM sends jobs to the local scheduler and local scheduler schedule the job to the local grid site. Local grid site executes the jobs and sends the result back to the CDAM. Success full completion of the job status and resource status are updated into the auction history database. CDAM collect the result from all local grid site and return to the grid users. Results: The CDAM was simulated using grid simulator. Number of jobs increases then the percentage of the jobs accepted also decrease due to the scarcity of resources. CDAM is providing 2% to 5% better result than Fair share Meta scheduling algorithm. CDAM algorithm bid density value is generated based on the user requirement and user history and ask value is generated from the resource details. Users who, having the most significant deadline are generated the highest bid value, grid resource which is having the fastest processor are generated lowest ask value. The highest bid is assigned to the lowest Ask it means that the user who is having the most significant deadline is assigned to the grid resource which is having the fastest processor. The deadline represents a time by which the user requires the result. The user can define the deadline by which the results are needed, and the CDAM will try to find the fastest resource available in order to meet the user-defined deadline. If the scheduler detects that the tasks cannot be completed before the deadline, then the scheduler abandons the current resource, tries to select the next fastest resource and tries until the completion of application meets the deadline. CDAM is providing 25% better result than grid way Meta scheduler this is because grid way Meta scheduler allocate jobs to the resource based on the first come first served policy. Conclusion: The proposed CDAM model was validated through simulation and was evaluated based on jobs accepted. The experimental results clearly show that the CDAM model maximizes the number of jobs accepted than conventional Meta scheduler. We conclude that a CDAM is highly effective meta-scheduler systems and can be used for an extraordinary situation where jobs have a combinatorial requirement.

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pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis

Computer Methods and Programs in Biomedicine ◽

10.1016/j.cmpb.2020.105796 ◽

2021 ◽

Vol 198 ◽

pp. 105796

Author(s):

Alain Jungo ◽

Olivier Scheidegger ◽

Mauricio Reyes ◽

Fabian Balsiger

Keyword(s):

Image Analysis ◽

Deep Learning ◽

Medical Image ◽

Medical Image Analysis ◽

Data Handling ◽

Python Package

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Transfer Learning for 3‐Dimensional Medical Image Analysis

Smart and Sustainable Intelligent Systems ◽

10.1002/9781119752134.ch11 ◽

2021 ◽

pp. 151-171

Author(s):

Sanket Singh ◽

Sarthak Jain ◽

Akshit Khanna ◽

Anupam Kumar ◽

Ashish Sharma

Keyword(s):

Image Analysis ◽

Transfer Learning ◽

Medical Image ◽

Medical Image Analysis ◽

3 Dimensional

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APL Based medical image analysis

ACM SIGAPL APL Quote Quad ◽

10.1145/570440.570498 ◽

2000 ◽

Vol 30 (4) ◽

pp. 176-185

Author(s):

Tilman P. Otto

Keyword(s):

Image Analysis ◽

Medical Image ◽

Medical Image Analysis

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Increase in polymorphonuclear myeloid-derived suppressor cells and regulatory T-cells in children with B-cell acute lymphoblastic leukemia

Scientific Reports ◽

10.1038/s41598-021-94469-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Asmaa M. Zahran ◽

Azza Shibl ◽

Amal Rayan ◽

Mohamed Alaa Eldeen Hassan Mohamed ◽

Amira M. M. Osman ◽

...

Keyword(s):

T Cells ◽

Acute Lymphoblastic Leukemia ◽

B Cell ◽

Lymphoblastic Leukemia ◽

Critical Role ◽

Suppressor Cells ◽

Healthy Controls ◽

Blast Cells ◽

Cell Acute Lymphoblastic Leukemia ◽

The Impact

AbstractOur study aimed to evaluate the levels of MDSCs and Tregs in pediatric B-cell acute lymphoblastic leukemia (B-ALL), their relation to patients’ clinical and laboratory features, and the impact of these cells on the induction response. This study included 31 pediatric B-ALL patients and 27 healthy controls. All patients were treated according to the protocols of the modified St. Jude Children’s Research Hospital total therapy study XIIIB for ALL. Levels of MDSCs and Tregs were analyzed using flow cytometry. We observed a reduction in the levels of CD4 + T-cells and an increase in both the polymorphonuclear MDSCs (PMN-MDSCs) and Tregs. The frequencies of PMN-MDSCs and Tregs were directly related to the levels of peripheral and bone marrow blast cells and CD34 + cells. Complete postinduction remission was associated with reduced percentages of PMN-MDSCs and Tregs, with the level of PMN-MDCs in this subpopulation approaching that of healthy controls. PMN-MDSCs and Tregs jointly play a critical role in maintaining an immune-suppressive state suitable for B-ALL tumor progression. Thereby, they could be independent predictors of B-ALL progress, and finely targeting both PMN-MDSCs and Tregs may be a promising approach for the treatment of B-ALL.

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TransMed: Transformers Advance Multi-Modal Medical Image Classification

Diagnostics ◽

10.3390/diagnostics11081384 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1384

Author(s):

Yin Dai ◽

Yifan Gao ◽

Fayu Liu

Keyword(s):

Image Analysis ◽

Image Classification ◽

Long Range ◽

Medical Image ◽

Large Scale ◽

Medical Images ◽

Medical Image Analysis ◽

Lesion Detection ◽

Tumor Segmentation ◽

Medical Image Classification

Over the past decade, convolutional neural networks (CNN) have shown very competitive performance in medical image analysis tasks, such as disease classification, tumor segmentation, and lesion detection. CNN has great advantages in extracting local features of images. However, due to the locality of convolution operation, it cannot deal with long-range relationships well. Recently, transformers have been applied to computer vision and achieved remarkable success in large-scale datasets. Compared with natural images, multi-modal medical images have explicit and important long-range dependencies, and effective multi-modal fusion strategies can greatly improve the performance of deep models. This prompts us to study transformer-based structures and apply them to multi-modal medical images. Existing transformer-based network architectures require large-scale datasets to achieve better performance. However, medical imaging datasets are relatively small, which makes it difficult to apply pure transformers to medical image analysis. Therefore, we propose TransMed for multi-modal medical image classification. TransMed combines the advantages of CNN and transformer to efficiently extract low-level features of images and establish long-range dependencies between modalities. We evaluated our model on two datasets, parotid gland tumors classification and knee injury classification. Combining our contributions, we achieve an improvement of 10.1% and 1.9% in average accuracy, respectively, outperforming other state-of-the-art CNN-based models. The results of the proposed method are promising and have tremendous potential to be applied to a large number of medical image analysis tasks. To our best knowledge, this is the first work to apply transformers to multi-modal medical image classification.

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