Сellular pathology detection

Goal

Develop a solution of a medical diagnostic task based on neural networks with a team of schoolchildren within a 3-week shift in the educational center ‘Sirius’. The service should accurately detect the presence of cellular anomalies by micrograph of a living tissue section.

Our solution

During preparation, more than 1000 photographs of buccal epithelium samples were taken.

An application was developed In order to artificially increase the number of photos by rotating and adding noise.

The total size of the photo database is 22 000 images.

Photo marking was done manually with the subsequent use of regular expressions. Four main cell types were distinguished:

Normal
cell

Binucleated
cell

Cell with a
micronucleus

Cell with a
protrusion

The solution allowed to accurately determine the percentage of cells with anomalies.

The results for people with different smoking experience:

Smoking experience: 0 years

Smoking experience: 20 years

The solution will allow to:

  • Minimize the human factor influence

  • Speed up image studying by more than 300 times

  • Get rid of a large amount of documentation for each study

  • Standardize the process of studying cellular anomalies

The team involved in the service development

Leaders:

Bazarnyi Vladimir Viktorovich (doctor)
Maksimova Arina Yurievna (MLS)

Consultant:

Alperin Yakov Sergeevich (IT Specialist)

Team of students:

Ershova Ksenia

Photo layout, dataset creation

Tonkoshkurov Nikita

Neural network training,
web server creation

Kiseleva Ekaterina

Photo archive creation

Sarapulova Arina

Photo archive creation

Shcherbakova Maria

Photo archive creation

Semenov Arseny

Neural network training,
web server creation

Chermnykh Dmitry

Neural network training,
web server creation

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