NUMERO: #20220081

Título: MACHINE LEARNING IN THE DIAGNOSIS OF FOLLICULAR LYMPHOMA

Nome do Apresentador: Lucas Lacerda de SOUZA

Categoria do Trabalho: Apresentação Oral de Pôster de Pesquisa Científica (PPC)

Área Temática: Patologia Oral

Resumo: Objective: To implement a machine-learning (ML) model to assist pathologists in the differentiation of follicular hyperplasia (FH) and follicular lymphoma (FL).
Study Design: Whole slide images from 10 patients with FH and 10 patients with FL were manually annotated and fragmented into 21,585 patches (FH=13,399 and FL=8,186) of 299x299 pixels. The convolutional neural network (CNN) VGGNet was re-trained using Python 3.6 and other open-source libraries for machine learning and image processing (TensorFlow, Keras, Scikit-Learn, and OpenCV). The training and validation were carried out for 10 epochs until accuracy stabilized and validation loss reduced its variation.
Results: The total processing time for the CNN training was 753s. Different metrics could be obtained through the confusion matrix, emphasizing a high training accuracy of 98% and F1-score of 82%. Sensitivity and specificity were 74.8% and 91.8%, respectively. The receiver operating characteristic curve of 94% showed the fine class separation ability of the CNN.
Conclusion: The ML model used in this study is feasible to differentiate FH and FL. Additional CNN training and validation in bigger/multicentre datasets may generate AI-assisted tools to aid FL diagnosis.

Autor 1:  Lucas Lacerda de SOUZA

E-mail 1:  [email protected]

Autor 2:   Anna Luiza Damaceno ARAUJO

E-mail 2:  [email protected]

Autor 3 :  Viviane Mariano da SILVA

E-mail 3:  [email protected]

Autor 4:  Alan Roger Santos-Silva

E-mail 4:  [email protected]

Autor 5:  Marcio Ajudarte Lopes

E-mail 5:  [email protected]

Autor 6:  Matheus Cardoso Moraes

E-mail 6:  [email protected]

Autor 7:  Pablo Agustin Vargas

E-mail 7:  [email protected]