Method and device for identifying abnormal cell in to-be-detected sample, and storage medium

An apparatus for identifying an abnormal cell in a to-be-detected sample, the apparatus comprising:

• a memory storing instructions;
• and
• a processor in communication with the memory, wherein, when the processor executes the instructions, the processor is configured to cause the apparatus to perform:
• obtaining multi-layer images of a to-be-detected sample, the to-be-detected sample comprising a single cell and a cell cluster,
• obtaining multi-layer image blocks of the single cell and multi-layer image blocks of the cell cluster according to the multi-layer images,
• obtaining a first identification result by a first image identification network according to the multi-layer image blocks of the single cell,
• obtaining a second identification result by a second image identification network according to the multi-layer image blocks of the cell cluster, including:
• selecting at least one layer of image blocks from the multi-layer image blocks of the cell cluster, and inputting the at least one layer of image blocks into a first part of the second image identification network to obtain a second single-layer identification result corresponding to the multi-layer image blocks of the cell cluster;
• and
• inputting the multi-layer image blocks of the cell cluster into a second part of the second image identification network to obtain a second multi-layer identification result corresponding to the multi-layer image blocks of the cell cluster, and
• determining whether an abnormal cell exists in the to-be-detected sample according to the first identification result and the second identification result.

A non-transitory computer-readable storage medium, storing computer-readable instructions, wherein, the computer-readable instructions, when executed by a processor, are configured to cause the processor to perform:

• obtaining multi-layer images of a to-be-detected sample, the to-be-detected sample comprising a single cell and a cell cluster;
• obtaining multi-layer image blocks of the single cell and multi-layer image blocks of the cell cluster according to the multi-layer images;
• obtaining a first identification result by a first image identification network according to the multi-layer image blocks of the single cell;
• obtaining a second identification result by a second image identification network according to the multi-layer image blocks of the cell cluster, including:
• selecting at least one layer of image blocks from the multi-layer image blocks of the cell cluster, and inputting the at least one layer of image blocks into a first part of the second image identification network to obtain a second single-layer identification result corresponding to the multi-layer image blocks of the cell cluster;
• and
• inputting the multi-layer image blocks of the cell cluster into a second part of the second image identification network to obtain a second multi-layer identification result corresponding to the multi-layer image blocks of the cell cluster;
• and
• determining whether an abnormal cell exists in the to-be-detected sample according to the first identification result and the second identification result.