Quantifying and Visualizing Hidden Preferential Aggregations Amid Heterogeneity
Biological systems often exhibit a heterogeneous arrangement of objects, such as assorted nuclear chromatin patterns in a tumor, assorted species of bacteria in biofilms, or assorted aggregates of subcellular particles. Principle Component Analysis (PCA) and Multiple Component Analysis (MCA) provide information about which features in multidimensional data aggregate, but do not provide in situ spatial information about these aggregations. This paper outlines the Numericized Histogram Score (NHS) algorithm, which converts the histogram distribution of shortest distances between objects into a continuous variable that can be represented as a spatial heatmap. A histogram can be transformed into an intensity value by assigning a weighting factor to each sequential bin. Each object in an image can be replaced by its NHS value, which when calibrated to a color scale results in a heatmap. These spatial heatmaps reveal regions of aggregation amid heterogeneity that would otherwise mask loco-regional spatial associations, which will be especially useful in the field of digital pathology. In addition to visualizing aggregations as heatmaps, the ability to calculate degrees of recurring patterns of aggregation allows investigators to stratify samples for further insights into clinical outcome, response to treatment, or omic subtypes (genomic, transcriptomic, proteomic, metabolomic, etc.).
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Visualizing the Passage of Time with Video Temporal Pyramids
What can we learn about a scene by watching it for months or years? A video recorded over a long timespan will depict interesting phenomena at multiple timescales, but identifying and viewing them presents a challenge. T…
Towards Explainable TOPSIS: Visual Insights into the Effects of Weights and Aggregations on Rankings
Multi-Criteria Decision Analysis (MCDA) is extensively used across diverse industries to assess and rank alternatives. Among numerous MCDA methods developed to solve real-world ranking problems, TOPSIS remains one of the…
Pyramid Multi-view Stereo Net with Self-adaptive View Aggregation
n this paper, we propose an effective and efficient pyramid multi-view stereo (MVS) net with self-adaptive view aggregation for accurate and complete dense point cloud reconstruction. Different from using mean square var…
3D Point Cloud Reconstruction3D ReconstructionDepth EstimationPoint cloud reconstructionQuantifying the Preferential Direction of the Model Gradient in Adversarial Training With Projected Gradient Descent
Adversarial training, especially projected gradient descent (PGD), has proven to be a successful approach for improving robustness against adversarial attacks. After adversarial training, gradients of models with respect…
HOLA: Enhancing Audio-visual Deepfake Detection via Hierarchical Contextual Aggregations and Efficient Pre-training
Advances in Generative AI have made video-level deepfake detection increasingly challenging, exposing the limitations of current detection techniques. In this paper, we present HOLA, our solution to the Video-Level Deepf…
DeepFake Detection