Kakao Brain (CEOs Kim Byeong-hak and Kim Il-du) announced on the nineteenth that that they had released the 'Labeler Project', a medical image evaluation specialist that extracts specific disease names from chest X-ray evaluation, on GitHub.

The Labeler project is a research and development project that extracts specific disease names from various forms of natural language readings. It’s characterised by with the ability to extract a complete of 13 diseases that could be diagnosed based on chest

The reason is that when a user inputs a reading written in natural language, the evaluation is performed and the user is notified whether the 13 diseases are positive or negative.

Kakao Brain explained that as a way to contribute to improving medical diagnosis, it has begun research on a labeler project that extracts specific disease names more accurately and efficiently than other firms' models. This project has also been used to check the interior performance of Kakao Brain's chest X-ray draft interpretation technology.

In-house, we measured the accuracy of extracting disease names for 10 actual diseases, including fracture, pneumothorax, and pulmonary edema, and recorded the next accuracy (90.39%) than other firms' models (about 76%).

The project was also released on GitHub on the sixteenth. At the top of January, a paper titled ‘Improved Chest

Meanwhile, Kakao Brain announced that, just like the open source project 'Honeybee' released in January, this labeler was also released to revitalize the AI ​​open source ecosystem.

Kim Il-du, CEO of Kakao Brain, said, “We plan to release a test set we created in order that many researchers can use the Labeler project as a test benchmark.” He added, “We’ll use the Kakao Brain language model to enhance performance by moreover learning chest X-ray data.” “We plan to enhance it further,” he said.

Reporter Jang Se-min semim99@aitimes.com