AI decision-support systems are used in a range of industries that base their decisions on information. AI explainability is a part of AI functionality that is responsible for explaining how the AI came up with a specific output. MobiDev experts used explainable AI for medical image processing and cancer screening
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Computer vision techniques are actively used in the processing of medical images, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Whole-Slide images (WSI). For example, WSI of human tissues can reach 21,500 × 21,500 pixel size and even more. However, since WSI is a really large image, it takes heaps of time, attention and qualification to analyze. Traditional computer vision methods will also require too much computational resources for end-to-end processing. So how can explainable AI help here?
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In terms of WSI analysis, explainable AI will act as a support system that scans image sectors and highlights regions of interest with suspicious cellular structures. The machine won’t make any decision, but will speed up the process and make the work of a doctor easier. This is possible because WSI is more accurate and quicker in terms of image scanning, giving less chance to omit specific regions. AI support system highlights the regions of high risk where cancer cells are more likely to be. This eliminates the need to physically analyze the whole image of a kidney, providing hints for medical expertise and attention.
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