Bone marrow cell classification method and system, computer equipment and storage medium
A bone marrow cell and classification method technology, applied in the field of bone marrow cell classification, can solve the problems that the identification experience of doctors cannot be well expressed, the classification accuracy of traditional methods is limited, and the degree of similarity between features is high. Detection effect, high classification accuracy effect
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Embodiment 1
[0077] Bone marrow cell classification work is often regarded as a coarse-grained classification task, and commonly used deep networks (such as ResNet) are used to classify myeloid cells. However, for practical application requirements, it is necessary to classify cells of the same genus into different types according to the maturity of bone marrow cells. Only the deep network designed for coarse-grained classification is used, and the classification accuracy is limited. To this end, this embodiment provides a method for classifying bone marrow cells.
[0078] like figure 1 As shown, the method for classifying bone marrow cells of the present embodiment includes the following steps:
[0079] S101. Acquire a first data set and a second data set.
[0080] Obtain the bone marrow cell microscopic image set of patients with different disease types as a data set, wherein the data set includes several bone marrow cell microscopic images of patients with different disease types, and...
Embodiment 2
[0132] like Figure 4 As shown, this embodiment provides a bone marrow cell classification system, which includes a first acquisition unit 401, a construction unit 402, a first training unit 403, a second training unit 404, a second acquisition unit 405, a prediction unit 406, The specific functions of the segmentation unit 407 and the classification unit 408 are as follows:
[0133] a first obtaining unit 401, configured to obtain a first data set and a second data set;
[0134] A construction unit 402 is used to construct a bone marrow cell detection network and a multi-level feature learning network based on feature fusion;
[0135] The first training unit 403 is used to select the YOLOX network as the bone marrow cell detection network, and use the first data set to train the bone marrow cell detection network to obtain a trained bone marrow cell detection network;
[0136] The second training unit 404 is configured to use the second data set to train the multi-level fea...
Embodiment 3
[0143] like Figure 5 As shown, this embodiment provides a computer device, which includes a processor 502 , a memory, an input device 503 , a display device 504 and a network interface 505 connected through a system bus 501 . The processor 502 is used to provide computing and control capabilities, the memory includes a non-volatile storage medium 506 and an internal memory 507, the non-volatile storage medium 506 stores an operating system, computer programs and databases, and the internal memory 507 is The operating system and the computer program in the non-volatile storage medium 506 provide an environment for running the computer program. When the computer program is executed by the processor 502, the method for classifying the bone marrow cells of the above-mentioned embodiment 1 is implemented, as follows:
[0144] Obtain the first data set and the second data set;
[0145] Build a bone marrow cell detection network and a multi-level feature learning network based on f...
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