Abstract: Systems and methods for normalizing image data obtained using multispectral imaging are disclosed. In one aspect, an image analysis apparatus includes an imaging device configured to perform a multispectral scan of a tissue sample to generate image data, a processor, and at least one computer-readable memory. The processor is configured to receive the image data from the imaging device and preprocess the image data to generate a normalization factor based on pixel values of the image data within at least one sub-region of the image data. The processor is also configured to provide the preprocessed image data and the normalization factor as inputs to a machine learning algorithm and determine, based on an output of the machine learning algorithm, whether the image data is indicative of a disease present in the tissue sample.

Abstract: An apparatus and methods for determining features in an image of a Hematoxylin and Eosin (H&E) stained tissue sample. An apparatus can be configured to apply a machine learning model to the H&E stained tissue sample image to determine features in the image.

Abstract: Systems and methods for determining pixel classification information using images depicting at least a portion of a whole slide image (WSI) of a stained tissue sample. A system can store a first image of the tissue sample at a first resolution, a second image of the tissue sample at a second resolution that is higher than the first resolution, and a third image of the tissue sample at a third resolution that is higher than the second resolution, the first, second, and third images depicting at least a portion of a same area of the tissue sample. The system can include be configured to generate first feature information based on the first image, generate second feature information based on the second image, and determine pixel classification information of at least a portion of the WSI based on the third image, the first feature information and second feature information.

Abstract: Systems and methods for similarity learning in digital pathology are provided. In one aspect, an apparatus for generating training image data includes a hardware memory configured to store executable instructions and a hardware processor in communication with the hardware memory, wherein the executable instructions, when executed by the processor, cause the processor to obtain a plurality of histopathology images, classify two or more of the histopathology images as similar or dissimilar, and create a dataset of training image data including the classified histopathology images.