HematoNet: Expert level classification of bone marrow cytology morphology in hematological malignancy with deep learning

Tripathi, Satvik; Augustin, Alisha Isabelle; Sukumaran, Rithvik; Dheer, Suhani; Kim, Edward

doi:10.1016/j.ailsci.2022.100043

Cited by 10 publications

(2 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, brain tumor image segmentation was proposed utilizing CoAtNet 61 . Furthermore, CoAtNet was compared with EfficientNet-V2, and ResNext50 which are SOTA ConvNets and it was found to outperform them for bone marrow cells classification 62 .…”

Section: Methodsmentioning

confidence: 99%

Parasitic egg recognition using convolution and attention network

AlDahoul,

Karim,

Momo

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Intestinal parasitic infections (IPIs) caused by protozoan and helminth parasites are among the most common infections in humans in low-and-middle-income countries. IPIs affect not only the health status of a country, but also the economic sector. Over the last decade, pattern recognition and image processing techniques have been developed to automatically identify parasitic eggs in microscopic images. Existing identification techniques are still suffering from diagnosis errors and low sensitivity. Therefore, more accurate and faster solution is still required to recognize parasitic eggs and classify them into several categories. A novel Chula-ParasiteEgg dataset including 11,000 microscopic images proposed in ICIP2022 was utilized to train various methods such as convolutional neural network (CNN) based models and convolution and attention (CoAtNet) based models. The experiments conducted show high recognition performance of the proposed CoAtNet that was tuned with microscopic images of parasitic eggs. The CoAtNet produced an average accuracy of 93%, and an average F1 score of 93%. The finding opens door to integrate the proposed solution in automated parasitological diagnosis.

show abstract

Section: Methodsmentioning

confidence: 99%

Parasitic egg recognition using convolution and attention network

AlDahoul,

Karim,

Momo

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Li et al on the other hand, were able to differentiate human diffuse large B-cell lymphoma (DLBCL) and non-DLBCL with 100% accuracy using multiple CNNs to classify pathologic images [ 40 ]. A study undertaken on BMSs to identify and classify bone marrow cells showed that the convolution and attention network model (CoAtNet), a hybrid of CNNs and transformer models, showed the best performance (accuracy >95%) when compared to other models evaluated in a similar fashion [ 41 ]. AI-assisted BMS evaluation helps to predict mutations in hematologic malignancies.…”

Section: Current Applications Of Ai In Hematologic Cytologymentioning

confidence: 99%

Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders

Gedefaw

Liu

et al. 2023

Cells

View full text Add to dashboard Cite

Artificial intelligence (AI) is a rapidly evolving field of computer science that involves the development of computational programs that can mimic human intelligence. In particular, machine learning and deep learning models have enabled the identification and grouping of patterns within data, leading to the development of AI systems that have been applied in various areas of hematology, including digital pathology, alpha thalassemia patient screening, cytogenetics, immunophenotyping, and sequencing. These AI-assisted methods have shown promise in improving diagnostic accuracy and efficiency, identifying novel biomarkers, and predicting treatment outcomes. However, limitations such as limited databases, lack of validation and standardization, systematic errors, and bias prevent AI from completely replacing manual diagnosis in hematology. In addition, the processing of large amounts of patient data and personal information by AI poses potential data privacy issues, necessitating the development of regulations to evaluate AI systems and address ethical concerns in clinical AI systems. Nonetheless, with continued research and development, AI has the potential to revolutionize the field of hematology and improve patient outcomes. To fully realize this potential, however, the challenges facing AI in hematology must be addressed and overcome.

show abstract