Abstract: A method of detecting a biomarker by a detection system based on machine learning includes identifying, by the detection system, a plurality of tiles corresponding to whole-slide image data of a tissue sample; generating, by the detection system, tile-level embeddings data based on the plurality of tiles; generating, by the detection system, cell-level embeddings data based on the plurality of tiles; and generating, by the detection system, a slide-level prediction based on the tile-level embeddings data and the cell-level embeddings data, the slide-level prediction indicating presence or absence of the biomarker in the tissue sample.

Abstract: One or more devices, systems, methods, and storage mediums using artificial intelligence application(s) using an apparatus or system that uses and/or controls one or more imaging modalities, such as, but not limited to, angiography, Optical Coherence Tomography (OCT), Multi-modality OCT, near-infrared fluorescence (NIRF), OCT-NIRF, near-infrared auto-fluorescence (NIRAF), OCT-NIRAF, etc. are provided herein.

Abstract: One or more devices, systems, methods, and storage mediums using artificial intelligence application(s) using an apparatus or system that uses and/or controls one or more imaging modalities, such as, but not limited to, angiography, Optical Coherence Tomography (OCT), Multi-modality OCT, near-infrared fluorescence (NIRAF), OCT-NIRAF, etc. are provided herein.

Abstract: Systems and techniques that facilitate automated localization of large vessel occlusions are provided. In various embodiments, an input component can receive computed tomography angiogram (CTA) images of a patient's brain. In various embodiments, a localization component can determine, via a machine learning algorithm, a location of a large vessel occlusion (LVO) in the patient's brain based on the CTA images. In various instances, the location of the LVO can comprise a laterality and an occlusion site. In various aspects, the laterality can indicate a right side or a left side of the patient's brain, and the occlusion site can indicate an internal carotid artery (ICA), an M1 segment of a middle cerebral artery (MCA) or an M2 segment of an MCA. In various cases, a visualization component can generate and display to a user a three-dimensional maximum intensity projection (MIP) reconstruction of the patient's brain based on the CTA images to facilitate visual verification of the LVO by the user.

Abstract: One or more devices, systems, methods, and storage mediums using artificial intelligence application(s) using an apparatus or system that uses and/or controls one or more imaging modalities, such as, but not limited to, angiography, Optical Coherence Tomography (OCT), Multi-modality OCT, near-infrared fluorescence (NIRAF), OCT-NIRAF, etc. are provided herein.

Abstract: Systems and techniques that facilitate automated localization of large vessel occlusions are provided. In various embodiments, an input component can receive computed tomography angiogram (CTA) images of a patient's brain. In various embodiments, a localization component can determine, via a machine learning algorithm, a location of a large vessel occlusion (LVO) in the patient's brain based on the CTA images. In various instances, the location of the LVO can comprise a laterality and an occlusion site. In various aspects, the laterality can indicate a right side or a left side of the patient's brain, and the occlusion site can indicate an internal carotid artery (ICA), an M1 segment of a middle cerebral artery (MCA) or an M2 segment of an MCA. In various cases, a visualization component can generate and display to a user a three-dimensional maximum intensity projection (MIP) reconstruction of the patient's brain based on the CTA images to facilitate visual verification of the LVO by the user.