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Using toponomics to characterize phenotypic diversity in alveolar macrophages from male mice treated with exogenous SP-A1.

We used the Toponome Imaging System (TIS) to identify "patterns of marker expression", referred to here as combinatorial molecular phenotypes (CMPs) in alveolar macrophages (AM) in response to the innate immune molecule, SP-A1.

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MAPK Phospho-Specific Arr Influenza B (B Tokio 53 9 TGF-Beta Signaling Phosph Head & Neck cancer test t Cytokine (Human) Antibody Cytokine (Mouse) Antibody Angiogenesis (Human) Anti removed without changing Cytokine (Human) Antibody Cell cycle antibody array Cytokine (Mouse) Antibody Cancer Apoptosis Phospho-

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Tailored for Real-World: A Whole Slide Image Classification System Validated on Uncurated Multi-Site Data Emulating the Prospective Pathology Workload.

Standard of care diagnostic procedure for suspected skin cancer is microscopic examination of hematoxylin & eosin stained tissue by a pathologist. Areas of high inter-pathologist discordance and rising biopsy rates necessitate higher efficiency and diagnostic reproducibility. We present and validate a deep learning system which classifies digitized dermatopathology slides into 4 categories. The system is developed using 5,070 images from a single lab, and tested on an uncurated set of 13,537 images from 3 test labs, using whole slide scanners manufactured by 3 different vendors. The system's use of deep-learning-based confidence scoring as a criterion to consider the result as accurate yields an accuracy of up to 98%, and makes it adoptable in a real-world setting. Without confidence scoring, the system achieved an accuracy of 78%. We anticipate that our deep learning system will serve as a foundation enabling faster diagnosis of skin cancer, identification of cases for specialist review, and targeted diagnostic classifications.

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