Digital Pathology Image Analysis is a diagnostic method based on image information, known as the "gold standard" for disease diagnosis. Our data-driven artificial intelligence system uses image recognition technology to achieve high automation and help transform pathology diagnosis into digital diagnosis, which can effectively improve pathological diagnosis and break through industry bottlenecks.
Pathological diagnosis is a diagnostic method based on image information, known as the "gold standard" for disease diagnosis. At this stage, artificial intelligence is mainly used in cervical cancer screening for cell pathology in pathology, which has huge potential market value. Our artificial intelligence pathology analysis technology is driven by data and has high automation. It realizes the diagnosis and recognition of multiple diseases in the imaging field. The accuracy rate is more than 90%. It helps the pathological diagnosis to be converted into digital diagnosis, which can greatly shorten the workload of doctors and break through the bottlenecks in medical diagnosis such as scarce doctor resources, heavy repetitive workload, and uneven diagnosis quality. Our AI pathology analysis can not only improve the efficiency of medical staff, reduce medical costs, but also enable scientific and effective daily monitoring and prevention with the help of big data platforms.
The main function of our pathology analysis module is to exclude negative samples and identify positive areas, so as to assist pathologists to improve the efficiency of pathological diagnosis or replace manual diagnosis of certain diseases. The diagnosis process of our pathological analysis module mainly includes data preprocessing: the production of standardized slices and digital scanning; artificial intelligence reading; artificial intelligence marking positive slices and manual review and so on.
Figure 1 Workflow of AI pathology analysis
1 Digitization of pathological sections & data preprocessing
We use high-definition scanning equipment to convert multiple stained tissue samples into digital pathological slice images, and then use a series of image processing algorithm such as shadow correction and grayscale optimization to obtain standardized slice image data as the pathological analysis algorithm input data.
Figure 2 Digitization of pathological sections & data preprocessing
2 Artificial intelligence algorithm
The pathological analysis algorithm model is based on a 152-layer deep neural network built by TensorFlow, which classification accuracy of pathological image is as high as 98%. Also, we use TensorFlow serving to implement MapReduce multi-thread automatic processing, so that the pathological analysis algorithm model can be used to complete a large number of pathological image analysis work in a very short time and help users improve efficiency.
Figure 3 Artificial intelligence algorithm
Accurate
Our pathological analysis algorithm model is based on data-driven, in which the neural network model based on deep learning has an accuracy rate of up to 98%, which is more stable than manual analysis.
Efficient
Our pathological analysis algorithm model uses fully automatic multi-threaded processing, runs on a high-performance GPU server，which can accurately and quickly complete pathological slice image data analysis, greatly reducing the workload of doctors and improve efficiency.
Low Cost
Our pathological analysis algorithm model provides fully automatic AI-assisted pathological image recognition services, which can effectively solve the problems of low manual diagnosis efficiency, insufficient pathologists, and lack of unified quality control management, thereby greatly reducing the user's human and material costs.
CD ComputaBio has formed a team of experts excellent in imaging science and clinical domain knowledge, providing AI-driven solutions for Pathology Analysis according to your detailed requirements.
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