Accurate diagnosis of non-Hodgkin lymphoma on whole-slide images using deep learning

Khelil, Hathem; Zerari, Abd El Mouméne; Djerou, Leila

doi:10.1109/setit54465.2022.9875482

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…This demonstrates the need for further improvement in identifying features between these classes in order to improve model accuracy. 0.965 Inception V3 Network [17] 0.973 FusionNet [19] 0.976 Resnet50 [23] 0.981 Khelil [25] 0.987 Proposed technique 0.991 The results indicate that the model correctly caught the underlying patterns during the training process, as shown by its high accuracy ratings. Notably, the model's incredibly low loss value of 0.0080, as shown in Figure 8, demonstrates its ability to effectively diagnose errors using our technique.…”

Section: Experimental Results and Analysismentioning

confidence: 95%

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Augmented Image Dataset Using Image-to-Image Translation to Enhance the Non-Hodgkin Lymphoma Diagnosis

Zerari,

Khelil,

Djerou

et al. 2024

RIA

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Section: Experimental Results and Analysismentioning

confidence: 95%

“…Khelil and Djerou [25] propose an approach regarding digital pathology. The authors describe using an improved CNN algorithm along with deep learning approaches to develop a model for classifying non-Hodgkin lymphomas.…”

Section: Literature Reviewmentioning

confidence: 99%

Augmented Image Dataset Using Image-to-Image Translation to Enhance the Non-Hodgkin Lymphoma Diagnosis

Zerari,

Khelil,

Djerou

et al. 2024

RIA

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The Role of Artificial Intelligence and Machine Learning Methodologies in Bioinformatics

Shree,

Shiva Kumar,

Ganesan

et al. 2024

2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)

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Lymphoma triage from H&E using AI for improved clinical management

Tsakiroglou,

Bacon,

Shingleton

et al. 2023

J Clin Pathol

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AimsIn routine diagnosis of lymphoma, initial non-specialist triage is carried out when the sample is biopsied to determine if referral to specialised haematopathology services is needed. This places a heavy burden on pathology services, causes delays and often results in over-referral of benign cases. We aimed to develop an automated triage system using artificial intelligence (AI) to enable more accurate and rapid referral of cases, thereby addressing these issues.MethodsA retrospective dataset of H&E-stained whole slide images (WSI) of lymph nodes was taken from Newcastle University Hospital (302 cases) and Manchester Royal Infirmary Hospital (339 cases) with approximately equal representation of the 3 most prevalent lymphoma subtypes: follicular lymphoma, diffuse large B-cell and classic Hodgkin’s lymphoma, as well as reactive controls. A subset (80%) of the data was used for training, a further validation subset (10%) for model selection and a final non-overlapping test subset (10%) for clinical evaluation.ResultsAI triage achieved multiclass accuracy of 0.828±0.041 and overall accuracy of 0.932±0.024 when discriminating between reactive and malignant cases. Its ability to detect lymphoma was equivalent to that of two haematopathologists (0.925, 0.950) and higher than a non-specialist pathologist (0.75) repeating the same task. To aid explainability, the AI tool also provides uncertainty estimation and attention heatmaps.ConclusionsAutomated triage using AI holds great promise in contributing to the accurate and timely diagnosis of lymphoma, ultimately benefiting patient care and outcomes.

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Accurate diagnosis of non-Hodgkin lymphoma on whole-slide images using deep learning

Cited by 3 publications

References 16 publications

Augmented Image Dataset Using Image-to-Image Translation to Enhance the Non-Hodgkin Lymphoma Diagnosis

Augmented Image Dataset Using Image-to-Image Translation to Enhance the Non-Hodgkin Lymphoma Diagnosis

The Role of Artificial Intelligence and Machine Learning Methodologies in Bioinformatics

Lymphoma triage from H&E using AI for improved clinical management

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