Critique of optical coherence tomography in epistemological metrology

This treaties aims to introduce a speculative framework to comprehend modern metrology. In a classical sense, measurement measures physically existing quantities, such as length, mass, and time. By contrast, modern measurement measures a wide spectrum of objects, for example, an imaging biomarker, which is a user-defined pattern. Artificial-intelligence (AI)-based diagnosis is another example. If we regard an optical coherence tomography (OCT) device equipped with diagnostic AI as a comprehensive "measurement system," the object being measured is a "disease," which is not a physical quantity but a concept. To comprehend the wide range of modern measurements, we introduce a speculative, i.e., philosophical and theoretical, model called the "epistemological-metrology model." In this model, we describe the act of measurement as cascading encoding-and-decoding processes. In the encoding processes, three types of objects-to-be measured are considered, which include substance, existence, and concept. Then we classify acts of measurement into "sensing," "understanding," and "reasoning," which measure the substance, existence, and concept, respectively. We note that the measurements in the understanding and reasoning classes are constructive. Namely, they proactively define the quantity-to-be-measured by the measurement modalities themselves. A speculative method to warrant the relevance of such constructive measurements is presented. We investigate several modern OCT-related measurements, including AI-based diagnosis, types of polarization sensitive OCT, and attenuation coefficient imaging, using our theoretical framework

Multi-contrast Jones-matrix optical coherence tomography -- the concept, principle, implementation, and applications

Jones-matrix optical coherence tomography (JM-OCT) is an extension of OCT that provides multiple types of optical contrasts of biological and clinical samples. JM-OCT measures the spatial distribution of the Jones matrix of the sample and also its time sequence. All contrasts (i.e., multi-contrast OCT images) are then computed from the Jones matrix. The contrasts obtained from the Jones matrix include the conventional and polarization-insensitive OCT intensity, cumulative and local phase retardation (birefringence), degree-of-polarization uniformity quantifying the polarization randomness of the sample, signal attenuation coefficient, sample scatterer density, Doppler OCT, OCT angiography, and dynamic OCT. JM-OCT is a generalized version of OCT because it measures the generalized form of the sample information; i.e., the Jones matrix sequence. This review summarizes the basic conception, mathematical principle, hardware implementation, signal and image processing, and biological and clinical applications of JM-OCT. Advanced technical topics, including JM-OCT-specific noise correction and quantity estimation and JM-OCT's self-calibration nature, are also described

Damage Vision Mining Opportunity for Imbalanced Anomaly Detection

In past decade, previous balanced datasets have been used to advance algorithms for classification, object detection, semantic segmentation, and anomaly detection in industrial applications. Specifically, for condition-based maintenance, automating visual inspection is crucial to ensure high quality. Deterioration prognostic attempts to optimize the fine decision process for predictive maintenance and proactive repair. In civil infrastructure and living environment, damage data mining cannot avoid the imbalanced data issue because of rare unseen events and high quality status by improved operations. For visual inspection, deteriorated class acquired from the surface of concrete and steel components are occasionally imbalanced. From numerous related surveys, we summarize that imbalanced data problems can be categorized into four types; 1) missing range of target and label valuables, 2) majority-minority class imbalance, 3) foreground-background of spatial imbalance, 4) long-tailed class of pixel-wise imbalance. Since 2015, there has been many imbalanced studies using deep learning approaches that includes regression, image classification, object detection, semantic segmentation. However, anomaly detection for imbalanced data is not yet well known. In the study, we highlight one-class anomaly detection application whether anomalous class or not, and demonstrate clear examples on imbalanced vision datasets: wooden, concrete deterioration, and disaster damage. We provide key results on damage vision mining advantage, hypothesizing that the more effective range of positive ratio, the higher accuracy gain of anomaly detection application. Finally, the applicability of the damage learning methods, limitations, and future works are mentioned.Comment: 12 pages, 14 figures, 8 table

MN-Pair Contrastive Damage Representation and Clustering for Prognostic Explanation

For infrastructure inspections, damage representation does not constantly match the predefined classes of damage grade, resulting in detailed clusters of unseen damages or more complex clusters from overlapped space between two grades. The damage representation has fundamentally complex features; consequently, not all the damage classes can be perfectly predefined. The proposed MN-pair contrastive learning method helps to explore an embedding damage representation beyond the predefined classes by including more detailed clusters. It maximizes both the similarity of M-1 positive images close to an anchor and dissimilarity of N-1 negative images using both weighting loss functions. It learns faster than the N-pair algorithm using one positive image. We proposed a pipeline to obtain the damage representation and used a density-based clustering on a 2-D reduction space to automate finer cluster discrimination. We also visualized the explanation of the damage feature using Grad-CAM for MN-pair damage metric learning. We demonstrated our method in three experimental studies: steel product defect, concrete crack, and the effectiveness of our method and discuss future works.Comment: 8 pages, 10 figures, 3 table

River Surface Patch-wise Detector Using Mixture Augmentation for Scum-cover-index

Urban rivers provide a water environment that influences residential living. River surface monitoring has become crucial for making decisions about where to prioritize cleaning and when to automatically start the cleaning treatment. We focus on the organic mud, or "scum", that accumulates on the river's surface and contributes to the river's odor and has external economic effects on the landscape. Because of its feature of a sparsely distributed and unstable pattern of organic shape, automating the monitoring process has proved difficult. We propose a patch-wise classification pipeline to detect scum features on the river surface using mixture image augmentation to increase the diversity between the scum floating on the river and the entangled background on the river surface reflected by nearby structures like buildings, bridges, poles, and barriers. Furthermore, we propose a scum-index cover on rivers to help monitor worse grade online, collect floating scum, and decide on chemical treatment policies. Finally, we demonstrate the application of our method on a time series dataset with frames every ten minutes recording river scum events over several days. We discuss the significance of our pipeline and its experimental findings.Comment: 15 figures, 3 tabl