Lymphocyte detection for cancer analysis using a novel fusion block based channel boosted CNN

Rauf, Zunaira; Khan, Abdul Rehman; Sohail, Anabia; Alquhayz, Hani; Gwak, Jeonghwan; Khan, Asifullah

doi:10.1038/s41598-023-40581-z

Cited by 4 publications

(3 citation statements)

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“…Automated assessment of lymphocytes in histology images is challenging due to the complex nature of tissue representation. Such complexity often leads to a high percentage of false positives, as well as difficulties in detecting lymphocytes that appear in clusters or exhibit different morphologies [66]. To address these challenges, we have developed a novel framework for lymphocyte assessment.…”

Section: Methodsmentioning

confidence: 99%

CB-HVT Net: A Channel-Boosted Hybrid Vision Transformer Network for Lymphocyte Detection in Histopathological Images

Ali,

Rauf,

Khan

et al. 2023

IEEE Access

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Detection of Tumor-Infiltrating Lymphocytes (TILs) has a high prognostic value in cancer diagnosis due to their ability to identify and kill cancer cells. However, this task is non-trivial due to their diverse morphology, overlapping boundaries , and presence of artifacts. Vision Transformers (ViTs) have the ability to capture long-range relationships, but they lack local correlation in the images and require large training datasets. In this work, we propose a Channel Boosted Hybrid Vision Transformer (CB-HVT) to detect lymphocytes in histopathological images. The proposed network constitutes 1) a channel generation module, 2) a channel exploitation module, 3) a channel merging module, 4) a region-aware module, and 5) a detection and segmentation head. The proposed CB-HVT exploits the learning capacity of both CNN and ViT-based architectures to capture lymphocytic diverse morphology. In addition, we developed a feature fusion block to systematically and gradually merge the diverse feature maps to improve the learning capability of the network. The attention mechanism in the fusion block retains the most contributing features . We evaluated the effectiveness of the proposed CB-HVT on two publicly available datasets for lymphocyte detection in histopathological images . The proposed network showed good results as compared to the existing architectures in terms of F-Score (LYSTO: 0.88 and NuClick: 0.82). In addition, the performance of the proposed CB-HVT on an unseen test set reveals its significance as a valuable tool for pathologists for real-time lymphocyte detection.

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Section: Methodsmentioning

confidence: 99%

CB-HVT Net: A Channel-Boosted Hybrid Vision Transformer Network for Lymphocyte Detection in Histopathological Images

Ali,

Rauf,

Khan

et al. 2023

IEEE Access

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“…Pretrained models like AlexNet, GoogleNet and ResNET are employed for feature extraction and machine learning models are used for classification using Pap smear dataset for cervical cancer prediction [54]. Recently, the CNN model and its variants like Transformers [55] have been applied in medical image analysis [56] and Lymphocyte detection cancer patients [57].…”

Section: Related Workmentioning

confidence: 99%

Improving prediction of cervical cancer using KNN imputer and multi-model ensemble learning

Aljrees

2024

PLoS ONE

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Cervical cancer is a leading cause of women’s mortality, emphasizing the need for early diagnosis and effective treatment. In line with the imperative of early intervention, the automated identification of cervical cancer has emerged as a promising avenue, leveraging machine learning techniques to enhance both the speed and accuracy of diagnosis. However, an inherent challenge in the development of these automated systems is the presence of missing values in the datasets commonly used for cervical cancer detection. Missing data can significantly impact the performance of machine learning models, potentially leading to inaccurate or unreliable results. This study addresses a critical challenge in automated cervical cancer identification—handling missing data in datasets. The study present a novel approach that combines three machine learning models into a stacked ensemble voting classifier, complemented by the use of a KNN Imputer to manage missing values. The proposed model achieves remarkable results with an accuracy of 0.9941, precision of 0.98, recall of 0.96, and an F1 score of 0.97. This study examines three distinct scenarios: one involving the deletion of missing values, another utilizing KNN imputation, and a third employing PCA for imputing missing values. This research has significant implications for the medical field, offering medical experts a powerful tool for more accurate cervical cancer therapy and enhancing the overall effectiveness of testing procedures. By addressing missing data challenges and achieving high accuracy, this work represents a valuable contribution to cervical cancer detection, ultimately aiming to reduce the impact of this disease on women’s health and healthcare systems.

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“…It also reflected the inflammatory response, and hypoalbuminemia had strong predictive value for unfavorable outcomes in sepsis patients ( 29 ). Lymphocyte: a traditional biomarker widely employed to evaluate immunocompetence, lymphocyte count was highly relevant to sepsis prognosis ( 30 , 31 ). Lymphocytopenia was considered a predictor of impaired immunity and unfavorable outcomes in sepsis patients ( 32 ).…”

Section: Introductionmentioning

confidence: 99%

Clinical predictive value of the CRP-albumin-lymphocyte index for prognosis of critically ill patients with sepsis in intensive care unit: a retrospective single-center observational study

Zhang,

Zhao,

Liu

et al. 2024

Front. Public Health

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BackgroundSepsis is a complex syndrome characterized by physiological, pathological, and biochemical abnormalities caused by infection. Its development is influenced by factors such as inflammation, nutrition, and immune status. Therefore, we combined C-reactive protein (CRP), albumin, and lymphocyte, which could reflect above status, to be the CRP-albumin-lymphocyte (CALLY) index, and investigated its association with clinical prognosis of critically ill patients with sepsis.MethodsThis retrospective observational study enrolled critically ill patients with sepsis who had an initial CRP, albumin, and lymphocyte data on the first day of ICU admission. All data were obtained from the Affiliated Hospital of Jiangsu University. The patients were divided into quartiles (Q1–Q4) based on their CALLY index. The outcomes included 30-/60-day mortality and acute kidney injury (AKI) occurrence. The association between the CALLY index and these clinical outcomes in critically ill patients with sepsis was evaluated using Cox proportional hazards and logistic regression analysis.ResultsA total of 1,123 patients (63.0% male) were included in the study. The 30-day and 60-day mortality rates were found to be 28.1 and 33.4%, respectively, while the incidence of AKI was 45.6%. Kaplan–Meier analysis revealed a significant association between higher CALLY index and lower risk of 30-day and 60-day mortality (log-rank p < 0.001). Multivariate Cox proportional hazards analysis indicated that the CALLY index was independently associated with 30-day mortality [HR (95%CI): 0.965 (0.935–0.997); p = 0.030] and 60-day mortality [HR (95%CI): 0.969 (0.941–0.997); p = 0.032]. Additionally, the multivariate logistic regression model showed that the CALLY index served as an independent risk predictor for AKI occurrence [OR (95%CI): 0.982 (0.962–0.998); p = 0.033].ConclusionThe findings of this study indicated a significant association between the CALLY index and both 30-day and 60-day mortality, as well as the occurrence of AKI, in critically ill patients with sepsis. These findings suggested that the CALLY index may be a valuable tool in identifying sepsis patients who were at high risk for unfavorable outcomes.

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Lymphocyte detection for cancer analysis using a novel fusion block based channel boosted CNN

Cited by 4 publications

References 54 publications

CB-HVT Net: A Channel-Boosted Hybrid Vision Transformer Network for Lymphocyte Detection in Histopathological Images

CB-HVT Net: A Channel-Boosted Hybrid Vision Transformer Network for Lymphocyte Detection in Histopathological Images

Improving prediction of cervical cancer using KNN imputer and multi-model ensemble learning

Clinical predictive value of the CRP-albumin-lymphocyte index for prognosis of critically ill patients with sepsis in intensive care unit: a retrospective single-center observational study

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