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MobiDev Shares Research on WSI Processing for Cancer Detection

Modern histopathology involves multiple methods to analyze human cells, including tissue scans and anamnesis data, healthcare organizations rely on Whole Slide Imaging or WSI to save a digital scan of the entire histology result.

With their extensive expertise in computer vision, MobiDev approached the WSI processing system design through clustering. This is a constrained attention multiple instance learning (CLAM) approach. As a training dataset, the publicly available Clear Cell Renal Cell Carcinoma dataset was used.

The task was to create a model capable of analyzing WSI scans and then highlight regions of interest, or in other words, suspicious cells, found in the tissue. As long as healthcare is based on critical decisions, the role of AI in pathology is to support the doctor with hints to speed up the diagnostic process. However, the prediction should also be descriptive, as long as the user has to understand how the model derived this or that result. This is what’s called AI explainability as a feature of modern AI pathology systems.

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It can be seen that the CLAM’s attention network is quite interpretable. It outputs “importance scores” for each patch, so those can be visualized as a heatmap. If you take a closer look at the WSI on the right, you may see the affected cells colored blue, meaning the network determined they would affect the prediction the most.

Using neural networks to process weakly labeled WSI images provides a couple of advantages in this case. First of all, there can be discovered clinically relevant, but previously unrecognized morphological characteristics that have not been used in visual assessment by pathologists. Secondly, the model can recognize features that are beyond human perception, which can potentially provide more accurate diagnostic results and have a positive impact on surgical or therapeutic results.

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