Thyroid cell pathological section malignant region detection method based on deep learning

Abstract

The invention discloses a thyroid cell pathological section malignant region detection method based on deep learning. The method mainly comprises the following steps: performing pathological section on thyroid cells; carrying out digital processing on the image of the pathological section on a microscope, and smearing with different coloring agents to obtain a colored pathological section; cuttingthe complete pathological section into dices with proper sizes as the input of a deep neural network model; screening out invalid dices of the pathological sections; carrying out benign and malignantclassification on the pathological sections subjected to slicing and preliminary screening by adopting a weakly supervised learning method; constructing a random forest-based machine learning methodby utilizing a false positive removing scheme to remove false positive from a prediction result of benign and malignant classification; therefore, the detection accuracy can be further improved. A pathological section high-risk area display step includes normalizing the malignant prediction probability of each block and mapping the malignant prediction probability of each block into the original image to generate a thermodynamic diagram, and providing more intuitive visual display for pathologists.

Description

technical field [0001] The invention relates to the field of medical image processing, in particular to a deep learning-based method for detecting malignant regions of thyroid cell pathological sections. Background technique [0002] As the gold standard for pathological diagnosis, pathological slides play a very important role in both clinical and scientific research. Pathological screening of thyroid cells is in a stage of popularization. The pathological screening method of cell puncture has the advantages of less trauma, low risk and rapid diagnosis, and has been popularized in many tertiary hospitals. However, due to the late start of cytopathological screening, there are relatively few pathologists, and experienced doctors are scarce, resulting in a backlog of cases that need to be diagnosed. At the same time, in the diagnosis of cytopathological slides, there is often a problem that the target cells cannot be obtained or the target cells are too few, resulting in the...

AI Technical Summary

Problems solved by technology

However, due to the late start of cytopathological screening, there are relatively few pathologists, and experienced doctors are scarce, resulting in a backlog of cases that need to be diagnosed

At the same time, in the diagnosis of cytopathological slices, there is often a problem that the target cells cannot be obtained or the target cells are too few, resulting in the inability to diagnose

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