A Divide-and-Conquer Approach Towards Understanding Deep Networks

Deep neural networks have achieved tremendous success in various fields including medical image segmentation. However, they have long been criticized for being a black-box, in that interpretation, understanding and correcting architectures is difficult as there is no general theory for deep neural network design. Previously, precision learning was proposed to fuse deep architectures and traditional approaches. Deep networks constructed in this way benefit from the original known operator, have fewer parameters, and improved interpretability. However, they do not yield state-of-the-art performance in all applications. In this paper, we propose to analyze deep networks using known operators, by adopting a divide-and-conquer strategy to replace network components, whilst retaining networks performance. The task of retinal vessel segmentation is investigated for this purpose. We start with a high-performance U-Net and show by step-by-step conversion that we are able to divide the network into modules of known operators. The results indicate that a combination of a trainable guided filter and a trainable version of the Frangi filter yields a performance at the level of U-Net (AUC 0.974 vs. 0.972) with a tremendous reduction in parameters (111, 536 vs. 9, 575). In addition, the trained layers can be mapped back into their original algorithmic interpretation and analyzed using standard tools of signal processing

Pennsylvania Folklife Vol. 25, Folk Festival Supplement

• Quilts, Quilts, Quilts • America\u27s Heritage is Endowed with Contributions of the Pennsylvania Dutch • The Hospitality Tent: H is for Help - That\u27s What it\u27s all About • Pottery: A Folk Art Expressing the Most in Simplest Terms • It Never Rains on our Parade - On the Fourth of July • Vegetable Dyeing at the Kutztown Folk Festival • Festival Focus • Folk Festival Program • Festival Foods: The Original Touch of the Dutch • Ursinus College Studies at the Festival • Behind the Scenes of We Like Our Country, But We Love Our God • Reverse Glass Tinsel Painting • Tin, Tole and Independencehttps://digitalcommons.ursinus.edu/pafolklifemag/1069/thumbnail.jp

Multistate modeling of habitat dynamics: factors affecting Florida scrub transition probabilities

Many ecosystems are influenced by disturbances that create specific successional states and habitat structures that species need to persist. Estimating transition probabilities between habitat states and modeling the factors that influence such transitions have many applications for investigating and managing disturbance-prone ecosystems. We identify the correspondence between multistate capture-recapture models and Markov models of habitat dynamics. We exploit this correspondence by fitting and comparing competing models of different ecological covariates affecting habitat transition probabilities in Florida scrub and flatwoods, a habitat important to many unique plants and animals. We subdivided a large scrub and flatwoods ecosystem along central Florida\u27s Atlantic coast into 10-ha grid cells, which approximated average territory size of the threatened Florida Scrub-Jay (Aphelocoma coerulescens), a management indicator species. We used 1.0-m resolution aerial imagery for 1994, 1999, and 2004 to classify grid cells into four habitat quality states that were directly related to Florida Scrub-Jay source-sink dynamics and management decision making. Results showed that static site features related to fire propagation (vegetation type, edges) and temporally varying disturbances (fires, mechanical cutting) best explained transition probabilities. Results indicated that much of the scrub and flatwoods ecosystem was resistant to moving from a degraded state to a desired state without mechanical cutting, an expensive restoration tool. We used habitat models parameterized with the estimated transition probabilities to investigate the consequences of alternative management scenarios on future habitat dynamics. We recommend this multistate modeling approach as being broadly applicable for studying ecosystem, land cover, or habitat dynamics. The approach provides maximum-likelihood estimates of transition parameters, including precision measures, and can be used to assess evidence among competing ecological models that describe system dynamics

A comparative evaluation of the efficacy of manual, magnetostrictive and piezoelectric ultrasonic instruments: an in vitro profilometric and SEM study

OBJECTIVES: The debridement of diseased root surface is usually performed by mechanical scaling and root planing using manual and power driven instruments. Many new designs in ultrasonic powered scaling tips have been developed. However, their effectiveness as compared to manual curettes has always been debatable. Thus, the objective of this in vitro study was to comparatively evaluate the efficacy of manual, magnetostrictive and piezoelectric ultrasonic instrumentation on periodontally involved extracted teeth using profilometer and scanning electron microscope (SEM). MATERIAL AND METHODS: 30 periodontally involved extracted human teeth were divided into 3 groups. The teeth were instrumented with hand and ultrasonic instruments resembling clinical application. In Group A all teeth were scaled with a new universal hand curette (Hu Friedy Gracey After Five Vision curette; Hu Friedy, Chicago, USA). In Group B Cavitron(TM) FSI - SLI(TM) ultrasonic device with focused spray slimline inserts (Dentsply International Inc., York, PA, USA) were used. In Group C teeth were scaled with an EMS piezoelectric ultrasonic device with prototype modified PS inserts. The surfaces were analyzed by a Precision profilometer to measure the surface roughness (Ra value in µm) consecutively before and after the instrumentation. The samples were examined under SEM at magnifications ranging from 17x to 300x and 600x. RESULTS: The mean Ra values (µm) before and after instrumentation in all the three groups A, B and C were tabulated. After statistically analyzing the data, no significant difference was observed in the three experimental groups. Though there was a decrease in the percentage reduction of Ra values consecutively from group A to C. CONCLUSION: Within the limits of the present study, given that the manual, magnetostrictive and piezoelectric ultrasonic instruments produce the same surface roughness, it can be concluded that their efficacy for creating a biologically compatible surface of periodontally diseased teeth is similar

Cytologic scoring of equine exercise-induced pulmonary hemorrhage : performance of human experts and a deep learning-based algorithm

Exercise-induced pulmonary hemorrhage (EIPH) is a relevant respiratory disease in sport horses, which can be diagnosed by examination of bronchoalveolar lavage fluid (BALF) cells using the total hemosiderin score (THS). The aim of this study was to evaluate the diagnostic accuracy and reproducibility of annotators and to validate a deep learning-based algorithm for the THS. Digitized cytological specimens stained for iron were prepared from 52 equine BALF samples. Ten annotators produced a THS for each slide according to published methods. The reference methods for comparing annotator’s and algorithmic performance included a ground truth dataset, the mean annotators’ THSs, and chemical iron measurements. Results of the study showed that annotators had marked interobserver variability of the THS, which was mostly due to a systematic error between annotators in grading the intracytoplasmatic hemosiderin content of individual macrophages. Regarding overall measurement error between the annotators, 87.7% of the variance could be reduced by using standardized grades based on the ground truth. The algorithm was highly consistent with the ground truth in assigning hemosiderin grades. Compared with the ground truth THS, annotators had an accuracy of diagnosing EIPH (THS of < or ≥ 75) of 75.7%, whereas, the algorithm had an accuracy of 92.3% with no relevant differences in correlation with chemical iron measurements. The results show that deep learning-based algorithms are useful for improving reproducibility and routine applicability of the THS. For THS by experts, a diagnostic uncertainty interval of 40 to 110 is proposed. THSs within this interval have insufficient reproducibility regarding the EIPH diagnosis.The Dres. Jutta and Georg Bruns-Stifung für innovative Veterinärmedizin.https://journals.sagepub.com/home/vetCompanion Animal Clinical Studie

Bildbasierte Berechnung der Grundfrequenz für den Einsatz in der Videostroboskopie

Hintergrund: Die Videostroboskopie wird als Goldstandard in der Diagnostik der Stimmlippenschwingungen eingesetzt. Die Bestimmung des Lichtblitzabstandes durch das Audiosignal ist allerdings anfällig gegenüber Störgeräuschen, wie beispielsweise verbale Instruktionen des Untersuchenden. Dies erlaubt nur eingeschränkt die Grundfrequenzbestimmung. Diese ist jedoch essentiell für den optimalen Lichtblitzabstand. Wir erforschen einen neuen, in Echtzeit laufenden und KI-basierten Ansatz, der ausschließlich auf den Endoskopiebildern basiert.Material und Methoden: Die entwickelte KI-gestützte Methode nutzt Bildmaterial von Hochgeschwindigkeitskameras, um die Grundfrequenz der Stimmlippenschwingung auf endoskopischen Bildern zu bestimmen. Für jedes Bild berechnen wir durch ein tiefes neuronales Netz den relativen Öffnungsgrad der glottalen Fläche. Durch zufällig aufgenommene Bilder, sowie den daraus berechneten relativen Öffnungsgrad, können wir durch mathematische Verfahren ("compressed sensing") die Grundfrequenz berechnen. Unsere Methode wurde an gesunden Proband:innen getestet.Ergebnisse: Wir können zeigen, dass unser KI- und Bildbasierter Ansatz bei einer Aufnahmedauer von unter 600 ms die Grundfrequenz in über 95% der Fälle exakt berechnen kann. Die Datenanalyse unserer KI-Methode benötigt unter 75 ms und kann somit in Echtzeit bereitgestellt werden. Weiterhin wird beobachtet, dass die Aufnahme von Endoskopiebildern nicht strukturiert geschehen darf, so dass die Grundfrequenz adäquat bestimmt werden kann.Diskussion: Unsere Methode ist in der Lage sehr genau die Grundfrequenz zu bestimmen und stellt somit eine schnelle Alternative zur klassischen audiobasierten Videostroboskopie dar. Die Funktionsweise unserer Methode in pathophysiologischen Fällen wird in zukünftigen Studien untersucht werden.Fazit: Die laryngeale Videostroboskopie benötigt nicht per se Zugang zu fehlerfreien Audiodaten. Die einzigartige KI-gestützte Analyse einzelner Bilder erlaubt die Berechnung der Grundfrequenz und erlaubt eine neue, bildbasierte Videostroboskopie-Technologie

Histological mixed-type as an independent prognostic factor in stage I gastric carcinoma

The value of information is a general and broadly applicable concept that has been used for several decades to aid in making decisions in the face of uncertainty. Yet there are relatively few examples of its use in ecology and natural resources management, and almost none that are framed in terms of the future impacts of management decisions. In this paper we discuss the value of information in a context of adaptive management, in which actions are taken sequentially over a timeframe and both future resource conditions and residual uncertainties about resource responses are taken into account. Our objective is to derive the value of reducing or eliminating uncertainty in adaptive decision making. We describe several measures of the value of information, with each based on management objectives that are appropriate for adaptive management. We highlight some mathematical properties of these measures, discuss their geometries, and illustrate them with an example in natural resources management. Accounting for the value of information can conditions through time