Machine learning techniques for tertiary lymphoid structure (tls) detection

At least one non-transitory computer readable storage medium storing processor executable instructions that, when executed by at least one processor, cause the at least one processor to perform the method for using a trained neural network model to identify at least one tertiary lymphoid structure (TLS) in an image of tissue obtained from a subject having, at risk of having, or suspected of having cancer, the method comprising:

• obtaining a set of overlapping sub-images of the image of tissue;
• processing the set of overlapping sub-images using the trained neural network model to obtain a respective set of pixel-level sub-image masks, each of the set of pixel-level sub-image masks indicating, for each particular pixel of multiple individual pixels in a respective particular sub-image, a respective probability that the particular pixel is part of a tertiary lymphoid structure;
• determining a pixel-level mask for at least a portion of the image of the tissue covered by at least some of the sub-images in the set of overlapping sub-images, the determining comprising determining the pixel-level mask using at least some of the set of pixel-level sub-image masks corresponding to the at least some of the set of overlapping sub-images covering at least the portion of the image;
• identifying boundaries of at least one TLS in at least the portion of the image using the pixel-level mask;
• and
• identifying one or more features of the at least one TLS using the identified boundaries and at least the portion of the image.

A system, comprising:

• at least one computer hardware processor;
• and
• at least one non-transitory computer readable storage medium storing processor executable instructions that, when executed by at least one processor, cause the at least one processor to perform the method for using a trained neural network model to identify at least one tertiary lymphoid structure (TLS) in an image of tissue obtained from a subject having, at risk of having, or suspected of having cancer, the method comprising:
• obtaining a set of overlapping sub-images of the image of tissue;
• processing the set of overlapping sub-images using the trained neural network model to obtain a respective set of pixel-level sub-image masks, each of the set of pixel-level sub-image masks indicating, for each particular pixel of multiple individual pixels in a respective particular sub-image, a respective probability that the particular pixel is part of a tertiary lymphoid structure;
• determining a pixel-level mask for at least a portion of the image of the tissue covered by at least some of the sub-images in the set of overlapping sub-images, the determining comprising determining the pixel-level mask using at least some of the set of pixel-level sub-image masks corresponding to the at least some of the set of overlapping sub-images covering at least the portion of the image;
• identifying boundaries of at least one TLS in at least the portion of the image using the pixel-level mask;
• and
• identifying one or more features of the at least one TLS using the identified boundaries and at least the portion of the image.