IBM's recent achievements in the medical field are gratifying and moving. Following the successful diagnosis of skin cancer with photos, the IBM Research Institute recently released the latest results, using deep learning and neural networks to make great progress in identifying mitosis of cancerous cells.
When diagnosing cancerous cells, doctors mainly analyze the way patient tissue samples are analyzed by biopsy. However, even if these tissues are sometimes as tiny as needles, pathologists need to detect signs of tumor cell disappearance, and also observe the important features of cancerous cells to help doctors to prescribe the right medicine.
When the pathologist analyzes the sample, some typical tissue samples are color-coded with the reagent solution. The results show that the depth of the reagent color and its distribution in the cell tissue can distinguish the type of disease and the degree of disease deterioration.
Breast Cancer Cell Training Samples for the 2016 Cancer Diffusion Assessment Challenge
The pathologist then studied the labeled tissue sample under a microscope. However, this stage is time consuming and the workload is huge. The researchers have to deal with hundreds of samples every day, and such long time and high load work will inevitably lead to a decrease in the correct rate of diagnosis.
With the development of modern medical imaging technology and deep learning, pathologists are in urgent need of computer technology assistance, and computer scientists are making unremitting efforts. In order to verify the application of artificial intelligence technology in the medical field, scientists organized a hackathon challenge.
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