Cluttering Reduction for Interactive Navigation and Visualization of Historical Images

Iconographic representations, such as historical photos of geographic spaces, are precious cultural heritage resources capable of describing a particular geographical area’s evolution over time. These photographic collections may vary in size, between hundreds and thousands of items. With the advent of the digital era, many of these documents have been digitized, spatialized, and are available online. Browsing through these digital image collections represents new challenges. This paper examines the topic of historical image exploration in a virtual environment enabling the co-visualization of historical photos into a contemporary 3D scene. We address the topic of user interaction considering the potential volume of the input data. Our methodology is based on design guidelines that rely on visual perception techniques to ease visual complexity and improve saliency on specific cues. The designs are additionally implemented following an image-based rendering approach and evaluated in a group of users. Overall, these propositions may be a notable addition to creating innovative ways to visualize and discover historical images in a virtual geographic environment.

822 GraphITE: unsupervised graph embeddings approach to multiplex immunofluorescence image exploration reveals new insights into NSCLC and HNSCC tumor microenvironment

BackgroundPredictive biomarkers for response to IO therapies remain insufficient. Although multiplex immunofluorescence has the potential to provide superior biomarkers, the information garnered from these studies is frequently underleveraged. Due to the large number of markers that must be analyzed (6 - 40 +), and the complexity of the spatial information, the number of hypotheses is large and must be tested systematically and automatically. GraphITE (Graphs-based Investigation of Tissues with Embeddings) is a novel method of converting multiplex IF image analysis results into embeddings, numerical vectors which represent the phenotype of each cell as well as the immediate neighborhood. This allows for the clustering of embeddings based on similarity as well as the discovery of novel predictive biomarkers based on both the spatial and multimarker data in multiplex IF images. Here we demonstrate initial observations from deployment of GraphITE on 564 commercially-sourced NSCLC and HNSCC resections stained with a multiplex IF panel containing CD8, PDL1, PD1, CD68, Ki67, and CK.Methods4 μm FFPE tumor sections were stained with CD8, PDL1, PD1, CD68, Ki67, and CK at Akoya Biosciences using OPAL TSA-linked fluorophores and imaged on a Vectra Polaris. Images were analyzed by Computational Biology (AstraZeneca). Graphs were built by mapping each cell in the mIF image as a node, using the X, Y coordinates and connecting nodes with edges according to distance. 64-dimensional embeddings were generated using Deep Graph InfoMax (DGI).1 Embeddings are downprojected to 2 dimensions using UMAP.2. Details are available in the preprint of the GraphITE methods manuscript.3ResultsA single downprojection was developed using embeddings from 158 HNSCC and 406 NSCLC cases. 60–80 distinct clusters were observed, some of which contained embeddings from both indications and others which were exclusive to one indication. Exclusive clusters describe tissue neighborhoods observed only in one indication. Drivers of cluster exclusivity included increased cell density in HNSCC as compared to NSCLC both in PD-L1- tumor centers with few infiltrating lymphocytes as well as in PD-L1- macrophagedominated neighborhoods. HNSCC and NSCLC embeddings were more colocalized in PD-L1+ tumor centers and in tumor stroma with high CD8+ or CD68+ immune cell content and high PD-L1+ expression.ConclusionsThis study demonstrates the utility and potential of the GraphITE platform to discriminate between and describe both unique and common neighborhood-level features of the tumor microenvironment. Deploying GraphITE across multiple indications effectively leverages spatial heterogeneity and multimarker information from multiplex IF panels.References1. Veličković P, Fedus W, Hamilton WL, Liò P, Bengio Y, DevonHjelm R. Deep Graph Infomax. 2018. arxiv:1809.10341 [stat.ML].2. McInnes L, Healy J, Melville J. UMAP: Uniform manifold approximationand projection for dimension reduction. 2020; arxiv:1802.03426 [stat.ML].3. Innocenti C, Zhang Z, Selvaraj B, Gaffney I, Frangos M, Cohen-Setton J, Dillon LAL, Surace MJ, Pedrinaci C, Hipp J, Baykaner K. An unsupervised graph embeddings approach to multiplex immunofluorescence image explorationbioRxiv 2021.06.09.447654; doi: https://doi.org/10.1101/2021.06.09.447654Ethics ApprovalThe study was approved by AstraZeneca.

Perception of Faces and Elaboration of Gender and Victim/Aggressor Stereotypes: The Influence of Internet Use and of the Perceiver’s Personality

The use of social media, particularly among youngsters, is characterized by simple and fast image exploration, mostly of people, particularly faces. The study presented here was conducted in order to investigate stereotypical judgments about men and women concerning past events of aggression—perpetrated or suffered—expressed on the basis of their faces, and gender-related differences in the judgments. To this aim, 185 participants answered a structured questionnaire online. The questionnaire contained 30 photos of young people’s faces, 15 men and 15 women (Ma et al., 2015), selected on the basis of the neutrality of their expression, and participants were asked to rate each face with respect to masculinity/femininity, strength/weakness, and having a past of aggression, as a victim or as a perpetrator. Information about the empathic abilities and personality traits of participants were also collected. The results indicate that the stereotypes—both of gender and those of victims and perpetrators—emerge as a consequence of the visual exploration of faces that present no facial emotion. Some characteristics of the personality of the observers, such as neuroticism, extraversion, openness, conscientiousness, and affective empathy, have a role in facilitating or hindering stereotype processing, in different ways for male and female faces by male and female observers. In particular, both genders attribute their positive stereotypical attributes to same-gender faces: men see male faces as stronger, masculine, and more aggressive than women do, and women see female faces as more feminine, less weak, and less as victims than men do. Intensive use of social media emerges as a factor that could facilitate the expression of some stereotypes of violent experiences and considering female subjects as more aggressive. Findings in this study can contribute to research on aggressive behavior on the Internet and improve our understanding of the multiple factors involved in the elaboration of gender stereotypes relative to violent or victim behavior.

An Unsupervised Graph Embeddings Approach to Multiplex Immunofluorescence Image Exploration

Understanding the complex biology of the tumor microenvironment (TME) is necessary to understand the mechanisms of action of immuno-oncology therapies and to match the right therapies to the right patients. Multiplex immunofluorescence (mIF) is a useful technology that has tremendous potential to further our understanding of cancer patho-biology; however, tools that fully leverage the high dimensionality of this data are still in their infancy. We describe here a novel deep learning pipeline aimed to allow Graph-based Inspection of Tissues via Embeddings, GraphITE. GraphITE transforms mIF data into a graph representation, where unsupervised learning algorithms can be utilised to generate embeddings representing cellular `neighbourhoods'. The embeddings can be downprojected and explored for clustering analysis, and patterns can be mapped back to the image as well as interrogated for phenotypical, morphological, or structural distinctiveness. GraphITE supports the extraction of information not only on the phenotypes of individual cells or the relationships between specific cell types, but is able to characterize cell neighborhoods to look for more complex interactions, thereby allowing pathologists and data scientists to explore mIF data sets, uncovering patterns that are otherwise obscured by the high-dimensionality of the data. In this work, we showcase the current setup of the system, going from raw input data all the way to a user friendly exploration tool. Using this tool, we show how the data can be navigated in a way previously not possible.

Context-Preserving Volume Image Exploration Using A 3D Painting Metaphor

This thesis combines a 3D interaction model with a

Context-Preserving Volume Image Exploration Using A 3D Painting Metaphor

This thesis combines a 3D interaction model with a

ARt Image Exploration Space (ARIES): A response to the image needs of art library patrons

ARt Image Exploration Space (ARIES) is a free, cloud-based dynamic environment offering art historians and others an extensive array of practical tools for analysing images. It is the product of a successful collaboration between art historians, librarians, computer scientists, and engineers from the Frick Art Reference Library, New York University's Tandon School of Engineering, and Brazil's Universidade Federal Fluminense. ARIES is a powerful tool for art historians, both replicating and augmenting traditional methods they have long-used to study images.1 With the advent of the prevalent use of digital photos, art historians lacked the technology capable of replacing what they had previously been able to accomplish in the analogue world. Wood Ruby and Deutch realized that art historians needed an out-of-the-box solution that didn't require extensive knowledge of other disciplines (computer science and engineering). The result of successful collaborations and a generous donation, ARIES is now available in BETA form at www.artimageexplorationspace.com.

An Appraisal of Diverse Image Augmentation Methodologies

Image augmentation is very significant, challenging methods in image exploration. The intention of augmentation of the image is to expand the graphical image form, or to give a superior renovated illustration for imminent programmed image treating. Various images such as aerial images, images of satellite, medical images, etc., undergo for noise and meager contrast. Therefore, it is obligatory to eliminate the noise and to augment the contrast in order to upsurge image superiority. Among imperative phases in medical images exposure and investigation is an image development practice that develops the worth or lucidity of images for hominoid seeing, eliminating noise and blurring, rise contrast, and enlightening minutiae are cases of enrichment maneuvers. The augmentation practice varies from one arena to another rendering to its purpose. The present methods of image augmentation could be divided into 2 groupings: frequency, spatial domain enrichment. This work offers a brief summary of image augmentation methods in spatial territory. More precisely, paper classifies processing methods centered representative techniques of Image augmentation. Hence an impact of this paper is to categorize and analyze image augmentation methods, effort for evaluation of inadequacies and common desires in an arena of dynamic investigation and at last paper focuses on auspicious information on exploration for image augmentation for imminent study.