| Autor: |
Ruitao Zhang, Xueying Han, Zhengyang Lei, Chenyao Jiang, Ijaz Gul, Qiuyue Hu, Shiyao Zhai, Hong Liu, Lijin Lian, Ying Liu, Yongbing Zhang, Yuhan Dong, Can Yang Zhang, Tsz Kwan Lam, Yuxing Han, Dongmei Yu, Jin Zhou, Peiwu Qin |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Computers in biology and medicine. 150 |
| ISSN: |
1879-0534 |
| Popis: |
Acute leukemia is a type of blood cancer with a high mortality rate. Current therapeutic methods include bone marrow transplantation, supportive therapy, and chemotherapy. Although a satisfactory remission of the disease can be achieved, the risk of recurrence is still high. Therefore, novel treatments are demanding. Chimeric antigen receptor-T (CAR-T) therapy has emerged as a promising approach to treating and curing acute leukemia. To harness the therapeutic potential of CAR-T cell therapy for blood diseases, reliable cell morphological identification is crucial. Nevertheless, the identification of CAR-T cells is a big challenge posed by their phenotypic similarity with other blood cells. To address this substantial clinical challenge, herein we first construct a CAR-T dataset with 500 original microscopy images after staining. Following that, we create a novel integrated model called RCMNet (ResNet18 with Convolutional Block Attention Module and Multi-Head Self-Attention) that combines the convolutional neural network (CNN) and Transformer. The model shows 99.63% top-1 accuracy on the public dataset. Compared with previous reports, our model obtains satisfactory results for image classification. Although testing on the CAR-T cell dataset, a decent performance is observed, which is attributed to the limited size of the dataset. Transfer learning is adapted for RCMNet and a maximum of 83.36% accuracy is achieved, which is higher than that of other state-of-the-art models. This study evaluates the effectiveness of RCMNet on a big public dataset and translates it to a clinical dataset for diagnostic applications. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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