Autofluorescence Virtual Staining System for H&E Histology and Multiplex Immunofluorescence Applied to Immuno-Oncology Biomarkers in Lung Cancer.

Virtual staining for digital pathology has great potential to enable spatial biology research, improve efficiency and reliability in the clinical workflow, as well as conserve tissue samples in a non-destructive manner. In this study, we demonstrate the feasibility of generating virtual stains for hematoxylin and eosin (H&E) and a multiplex immunofluorescence (mIF) immuno-oncology panel (DAPI, PanCK, PD-L1, CD3, CD8) from autofluorescence images of unstained non-small cell lung cancer tissue by combining high-throughput hyperspectral fluorescence microscopy and machine learning. Using domain-specific computational methods, we evaluated the accuracy of virtual H&E for histologic subtyping and virtual mIF for cell segmentation-based measurements, including clinically-relevant measurements such as tumor area, T cell density, and PD-L1 expression (tumor proportion score and combined positive score).

HIPI: Spatially resolved multiplexed protein expression inferred from H&E WSIs.

Solid tumors are characterized by complex interactions between the tumor, the immune system and the microenvironment. These interactions and intra-tumor variations have both diagnostic and prognostic significance and implications. However, quantifying the underlying processes in patient samples requires expensive and complicated molecular experiments.

Principal Uncertainty Quantification With Spatial Correlation for Image Restoration Problems.

Uncertainty quantification for inverse problems in imaging has drawn much attention lately. Existing approaches towards this task define uncertainty regions based on probable values per pixel, while ignoring spatial correlations within the image, resulting in an exaggerated volume of uncertainty. In this paper, we propose PUQ (Principal Uncertainty Quantification) - a novel definition and corresponding analysis of uncertainty regions that takes into account spatial relationships within the image, thus providing reduced volume regions.

Finding a Landing Site in an Urban Area: A Multi-Resolution Probabilistic Approach.

This paper considers the problem of finding a landing spot for a drone in a dense urban environment. The conflicting requirements of fast exploration and high resolution are solved using a multi-resolution approach, by which visual information is collected by the drone at decreasing altitudes so that the spatial resolution of the acquired images increases monotonically. A probability distribution is used to capture the uncertainty of the decision process for each terrain patch.

Artificial intelligence for phase recognition in complex laparoscopic cholecystectomy.

Background: The potential role and benefits of AI in surgery has yet to be determined. This study is a first step in developing an AI system for minimizing adverse events and improving patient's safety. We developed an Artificial Intelligence (AI) algorithm and evaluated its performance in recognizing surgical phases of laparoscopic cholecystectomy (LC) videos spanning a range of complexities.