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Lenovo AI At Transform 2.0: Watch ‘Cutting Edge’ Software Discover Cancer Cells

Watch Lenovo's artificial intelligence software in action finding cancer cells to help physicians save lives with AI.

Lenovo has created what it calls “cutting edge” artificial intelligence software that detects cancer cells within a liver tumor to help save lives as the vendor pushes deeper into the healthcare market using artificial intelligence.

"Not only does this AI application detect that there is a tumor, but it shows me where the tumor is, the size of the tumor and if it is malignant versus benign," said David Mullin, worldwide Health Solution manager for Lenovo, in an interview with CRN at Lenovo Transform 2.0.

Lenovo specifically built its LeHealth AI application for data scientists as part of the Lenovo intelligent Computing Orchestration (LiCO) solution that simplifies the management and use of distributed clusters for High Performance Computing workloads and AI model development.

[Related: Lenovo Storage Hiring Spree Driving New NetApp Go-To-Market Strategy]

At Transform 2.0 in New York last week, LeHealth was on full display inside the conference's demo area. The demo use case leveraged Lenovo's ThinkStation P520 Workstation, a Lenovo certified Barco monitor and LeHealth to detect cancer cells in a liver tumor while also determining if it is benign or malignant.

Through advanced algorithms, a 3-D mesh is generated from patient CT, or CAT, scans to help physicians locate cancer cells and more easily manage patient diagnosis and information through a single interface. Healthcare professionals can rotate 3-D images of the patient's liver to view issues from all angles.

"It's real cutting-edge AI liver-tumor disease detection software," said Mullin. The AI detection application was developed in-house by Lenovo's AI Research Lab teams.

Lenovo's LeHealth won first place in the 2017 global Liver Tumor Segmentation Challenge (LiTS) out of hundreds of entries from 35 countries. The LiTS challenge is to develop automatic segmentation algorithms to segment liver lesions and enhance abdominal CT scans.

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