ISADS: Using images to detect melanoma

Researchers from the Intel lab in Pittsburgh have been working with physicians on a tool to assist them in diagnosing skin cancer. At IDF in San Francisco this week, they demonstrated the project. Once a digital photo of the skin lesion is captured, doctors can use the image to query for similar cases in a large database of skin lesions that have already been diagnosed. Having access to this collection of relevant knowledge, doctors will have more information to treat their patients.

ISADS stands for Interactive Search Assisted Decision Support. The goal is to enable doctors to make more informed decisions about a given case by presenting relevant annotated cases from large medical repositories. Unlike systems that make decisions for the physicians, ISADS is a tool for search and comparison to medically relevant annotated medical images, where retrieval is based on image content rather than text or metadata.

Researcher Mei Chen describes the project: One application we are working on is ISADS for melanoma detection. Melanoma is the most fatal kind of skin cancer, and its incidence has been increasing in the U.S. To develop an application for real clinical practice, we collaborate with physicians from University of Pittsburgh to make it fit into the clinical work flow. To achieve this we streamline and automate the operations as well as designing the interface to be physician-friendly with just the right features.

Automated interpretation of medical images is challenging. From removing artifacts to finding lesion boundaries to extracting color characteristics, we develop algorithms that are driven by domain-knowledge, employ computer vision techniques and statistical machine learning to keep improving the performance. Hopefully in time, this tool will enable doctors to more quickly and accurately diagnose melanoma.

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