Transformers for Microscopy Slide Image Segmentation of Invasive Melanoma

Prognosis for melanoma patients is traditionally determined with a tumor depth measurement called Breslow thickness. However, Breslow thickness fails to account for cross-sectional area, which is more useful for prognosis. We propose to use segmentation methods to estimate cross-sectional area of invasive melanoma in whole-slide images. First, we design a custom segmentation model from a transformer pretrained on breast cancer images, and adapt it for melanoma segmentation. Secondly, we finetune a segmentation backbone pretrained on natural images. Our proposed models produce quantitatively superior results compared to previous approaches and qualitatively better results as verified through a dermatologist.