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Hyperspectral imaging is capable of capturing information beyond conventional RGB cameras; therefore, several applications of this have been found, such as material identification and spectral analysis. However, similar to many camera systems, most of the existing hyperspectral cameras are still passive imaging systems. Such systems require an external light source to illuminate the objects, to capture the spectral intensity. As a result, the collected images highly depend on the environment lighting and the imaging system cannot function in a dark or low-light environment. This work develops a prototype system for active hyperspectral imaging, which actively emits diverse single-wavelength light rays at a specific frequency when imaging. This concept has several advantages: first, using the controlled lighting, the magnitude of the individual bands is more standardized to extract reflectance information; second, the system is capable of focusing on the desired spectral range by adjusting the number and type of LEDs; third, an active system could be mechanically easier to manufacture, since it does not require complex band filters as used in passive systems. Three lab experiments show that such a design is feasible and could yield informative hyperspectral images in low light or dark environments: (1) spectral analysis: this system's hyperspectral images improve food ripening and stone type discernibility over RGB images; (2) interpretability: this system's hyperspectral images improve machine learning accuracy. Therefore, it can potentially benefit the academic and industry segments, such as geochemistry, earth science, subsurface energy, and mining.
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Controllable writing and deleting of nanoscale magnetic skyrmions are key requirements for their use as information carriers for next-generation memory and computing technologies. While several schemes have been proposed, they require complex fabrication techniques or precisely tailored electrical inputs, which limits their long-term scalability. Here, we demonstrate an alternative approach for writing and deleting skyrmions using conventional electrical pulses within a simple, two-terminal wire geometry. X-ray microscopy experiments and micromagnetic simulations establish the observed skyrmion creation and annihilation as arising from Joule heating and Oersted field effects of the current pulses, respectively. The unique characteristics of these writing and deleting schemes, such as spatial and temporal selectivity, together with the simplicity of the two-terminal device architecture, provide a flexible and scalable route to the viable applications of skyrmions.
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BACKGROUND AND AIMS: EUS-guided needle-based confocal laser endomicroscopy (EUS-nCLE) can differentiate high-grade dysplasia/adenocarcinoma (HGD-Ca) in intraductal papillary mucinous neoplasms (IPMNs) but requires manual interpretation. We sought to derive predictive computer-aided diagnosis (CAD) and artificial intelligence (AI) algorithms to facilitate accurate diagnosis and risk stratification of IPMNs. METHODS: A post hoc analysis of a single-center prospective study evaluating EUS-nCLE (2015-2019; INDEX study) was conducted using 15,027 video frames from 35 consecutive patients with histopathologically proven IPMNs (18 with HGD-Ca). We designed 2 CAD-convolutional neural network (CNN) algorithms: (1) a guided segmentation-based model (SBM), where the CNN-AI system was trained to detect and measure papillary epithelial thickness and darkness (indicative of cellular and nuclear stratification), and (2) a reasonably agnostic holistic-based model (HBM) where the CNN-AI system automatically extracted nCLE features for risk stratification. For the detection of HGD-Ca in IPMNs, the diagnostic performance of the CNN-CAD algorithms was compared with that of the American Gastroenterological Association (AGA) and revised Fukuoka guidelines. RESULTS: Compared with the guidelines, both n-CLE-guided CNN-CAD algorithms yielded higher sensitivity (HBM, 83.3%; SBM, 83.3%; AGA, 55.6%; Fukuoka, 55.6%) and accuracy (SBM, 82.9%; HBM, 85.7%; AGA, 68.6%; Fukuoka, 74.3%) for diagnosing HGD-Ca, with comparable specificity (SBM, 82.4%; HBM, 88.2%; AGA, 82.4%; Fukuoka, 94.1%). Both CNN-CAD algorithms, the guided (SBM) and agnostic (HBM) models, were comparable in risk stratifying IPMNs. CONCLUSION: EUS-nCLE-based CNN-CAD algorithms can accurately risk stratify IPMNs. Future multicenter validation studies and AI model improvements could enhance the accuracy and fully automatize the process for real-time interpretation.
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Inteligência Artificial , Neoplasias Pancreáticas , Aspiração por Agulha Fina Guiada por Ultrassom Endoscópico , Humanos , Lasers , Microscopia Confocal , Redes Neurais de Computação , Neoplasias Pancreáticas/diagnóstico por imagem , Estudos Prospectivos , Medição de RiscoRESUMO
OBJECTIVE: To examine the robustness of the published machine learning models in the prediction of extraction vs non-extraction for a diverse US sample population seen by multiple providers. SETTING AND SAMPLE POPULATION: Diverse group of 838 patients (208 extraction, 630 non-extraction) were consecutively enrolled. MATERIALS AND METHODS: Two sets of input features (117 and 22) including clinical and cephalometric variables were identified based on previous studies. Random forest (RF) and multilayer perception (MLP) models were trained using these feature sets on the sample population and evaluated using measures including accuracy (ACC) and balanced accuracy (BA). A technique to identify incongruent data was used to explore underlying characteristics of the data set and split all samples into 2 groups (G1 and G2) for further model training. RESULTS: Performance of the models (75%-79% ACC and 72%-76% BA) on the total sample population was lower than in previous research. Models were retrained and evaluated using G1 and G2 separately, and individual group MLP models yielded improved accuracy for G1 (96% ACC and 94% BA) and G2 (88% ACC and 85% BA). RF feature ranking showed differences between top features for G1 (maxillary crowding, mandibular crowding and L1-NB) and G2 (age, mandibular crowding and lower lip to E-plane). CONCLUSIONS: An incongruent data pattern exists in a consecutively enrolled patient population. Future work with incongruent data segregation and advanced artificial intelligence algorithms is needed to improve the generalization ability to make it ready to support clinical decision-making.
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Inteligência Artificial , Aprendizado de Máquina , Algoritmos , Cefalometria , Humanos , Extração DentáriaRESUMO
It is challenging to obtain nanoscale resolution images in a single ultrafast shot because a large number of photons, greater than 1011, are required in a single pulse of the illuminating source. We demonstrate single-shot high resolution Fourier transform holography over a broad 7 µm diameter field of view with â¼ 5 ps temporal resolution. The experiment used a plasma-based soft X-ray laser operating at 18.9 nm wavelength with nearly full spatial coherence and close to diffraction-limited divergence implemented utilizing a dual-plasma amplifier scheme. A Fresnel zone plate with a central aperture is used to efficiently generate the object and reference beams. Rapid numerical reconstruction by a 2D Fourier transform allows for real-time imaging. A half-pitch spatial resolution of 62 nm was obtained. This single-shot nanoscale-resolution imaging technique will allow for real-time ultrafast imaging of dynamic phenomena in compact setups.
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Understanding thermal transport from nanoscale heat sources is important for a fundamental description of energy flow in materials, as well as for many technological applications including thermal management in nanoelectronics and optoelectronics, thermoelectric devices, nanoenhanced photovoltaics, and nanoparticle-mediated thermal therapies. Thermal transport at the nanoscale is fundamentally different from that at the macroscale and is determined by the distribution of carrier mean free paths and energy dispersion in a material, the length scales of the heat sources, and the distance over which heat is transported. Past work has shown that Fourier's law for heat conduction dramatically overpredicts the rate of heat dissipation from heat sources with dimensions smaller than the mean free path of the dominant heat-carrying phonons. In this work, we uncover a new regime of nanoscale thermal transport that dominates when the separation between nanoscale heat sources is small compared with the dominant phonon mean free paths. Surprisingly, the interaction of phonons originating from neighboring heat sources enables more efficient diffusive-like heat dissipation, even from nanoscale heat sources much smaller than the dominant phonon mean free paths. This finding suggests that thermal management in nanoscale systems including integrated circuits might not be as challenging as previously projected. Finally, we demonstrate a unique capability to extract differential conductivity as a function of phonon mean free path in materials, allowing the first (to our knowledge) experimental validation of predictions from the recently developed first-principles calculations.
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Precise characterization of the mechanical properties of ultrathin films is of paramount importance for both a fundamental understanding of nanoscale materials and for continued scaling and improvement of nanotechnology. In this work, we use coherent extreme ultraviolet beams to characterize the full elastic tensor of isotropic ultrathin films down to 11 nm in thickness. We simultaneously extract the Young's modulus and Poisson's ratio of low-k a-SiC:H films with varying degrees of hardness and average network connectivity in a single measurement. Contrary to past assumptions, we find that the Poisson's ratio of such films is not constant but rather can significantly increase from 0.25 to >0.4 for a network connectivity below a critical value of â¼2.5. Physically, the strong hydrogenation required to decrease the dielectric constant k results in bond breaking, lowering the network connectivity, and Young's modulus of the material but also decreases the compressibility of the film. This new understanding of ultrathin films demonstrates that coherent EUV beams present a new nanometrology capability that can probe a wide range of novel complex materials not accessible using traditional approaches.
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Beamline 2.1 (XM-2) is a transmission soft X-ray microscope in sector 2 of the Advanced Light Source at Lawrence Berkeley National Laboratory. XM-2 was designed, built and is now operated by the National Center for X-ray Tomography as a National Institutes of Health Biomedical Technology Research Resource. XM-2 is equipped with a cryogenic rotation stage to enable tomographic data collection from cryo-preserved cells, including large mammalian cells. During data collection the specimen is illuminated with `water window' X-rays (284-543â eV). Illuminating photons are attenuated an order of magnitude more strongly by biomolecules than by water. Consequently, differences in molecular composition generate quantitative contrast in images of the specimen. Soft X-ray tomography is an information-rich three-dimensional imaging method that can be applied either as a standalone technique or as a component modality in correlative imaging studies.
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Criopreservação , Imageamento Tridimensional/métodos , Microscopia de Fluorescência/instrumentação , Microscopia de Fluorescência/métodos , Animais , Biologia Celular/instrumentação , Células Cultivadas , Criopreservação/instrumentação , Criopreservação/métodos , Desenho de Equipamento , Luz , Manejo de Espécimes , Tomografia Computadorizada por Raios X/métodosRESUMO
We recorded the fast oscillation of sub-micron cantilevers using time-resolved extreme ultraviolet (EUV) Fourier transform holography. A tabletop capillary discharge EUV laser with a wavelength of 46.9 nm provided a large flux of coherent illumination that was split using a Fresnel zone plate to generate the object and the reference beams. The reference wave was produced by the first order focus while a central opening in the zone plate provided a direct illumination of the cantilevers. Single-shot holograms allowed for the composition of a movie featuring the fast oscillation. Three-dimensional displacements of the object were determined as well by numerical back-propagation, or "refocusing" of the electromagnetic fields during the reconstruction of a single hologram.
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A self-contained electro-optical module for scanning extreme ultraviolet (EUV) reflection microscopy at 13.5 nm wavelength has been developed. The system has been designed to work with stand-alone commercially available EUV high harmonic generation (HHG) sources through the implementation of narrowband harmonic selecting multilayers and off-axis elliptical short focal length zoneplates. The module has been successfully integrated into an EUV mask scanning microscope achieving diffraction limited imaging performance (84 nm point spread function).
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We demonstrate single and multi-shot Fourier transform holography with the use of a tabletop extreme ultraviolet laser. The reference wave was produced by a Fresnel zone plate with a central opening that allowed the incident beam to illuminate the sample directly. The high reference wave intensity allows for larger objects to be imaged compared to mask-based lensless Fourier transform holography techniques. We obtain a spatial resolution of 169 nm from a single laser pulse and a resolution of 128 nm from an accumulation of 20 laser pulses for an object ~11x11µm(2) in size. This experiment utilized a tabletop extreme ultraviolet laser that produces a highly coherent ~1.2 ns laser pulse at 46.9 nm wavelength.
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Holografia/instrumentação , Imageamento Tridimensional/instrumentação , Lasers , Refratometria/instrumentação , Desenho de Equipamento , Análise de Falha de Equipamento , Análise de Fourier , Raios UltravioletaRESUMO
We demonstrate the first general tabletop EUV coherent microscope that can image extended, non-isolated, non-periodic, objects. By implementing keyhole coherent diffractive imaging with curved mirrors and a tabletop high harmonic source, we achieve improved efficiency of the imaging system as well as more uniform illumination at the sample, when compared with what is possible using Fresnel zone plates. Moreover, we show that the unscattered light from a semi-transparent sample can be used as a holographic reference wave, allowing quantitative information about the thickness of the sample to be extracted from the retrieved image. Finally, we show that excellent tabletop image fidelity is achieved by comparing the retrieved images with scanning electron and atomic force microscopy images, and show superior capabilities in some cases.
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Lentes , Iluminação/instrumentação , Microscopia/instrumentação , Refratometria/instrumentação , Desenho de Equipamento , Análise de Falha de EquipamentoRESUMO
Pancreatic cancer is projected to become the second leading cause of cancer-related mortality in the United States by 2030. This is in part due to the paucity of reliable screening and diagnostic options for early detection. Amongst known pre-malignant pancreatic lesions, pancreatic intraepithelial neoplasia (PanIN) and intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent. The current standard of care for the diagnosis and classification of pancreatic cystic lesions (PCLs) involves cross-sectional imaging studies and endoscopic ultrasound (EUS) and, when indicated, EUS-guided fine needle aspiration and cyst fluid analysis. However, this is suboptimal for the identification and risk stratification of PCLs, with accuracy of only 65-75% for detecting mucinous PCLs. Artificial intelligence (AI) is a promising tool that has been applied to improve accuracy in screening for solid tumors, including breast, lung, cervical, and colon cancer. More recently, it has shown promise in diagnosing pancreatic cancer by identifying high-risk populations, risk-stratifying premalignant lesions, and predicting the progression of IPMNs to adenocarcinoma. This review summarizes the available literature on artificial intelligence in the screening and prognostication of precancerous lesions in the pancreas, and streamlining the diagnosis of pancreatic cancer.
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Despite the increasing rate of detection of incidental pancreatic cystic lesions (PCLs), current standard-of-care methods for their diagnosis and risk stratification remain inadequate. Intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent PCLs. The existing modalities, including endoscopic ultrasound and cyst fluid analysis, only achieve accuracy rates of 65-75% in identifying carcinoma or high-grade dysplasia in IPMNs. Furthermore, surgical resection of PCLs reveals that up to half exhibit only low-grade dysplastic changes or benign neoplasms. To reduce unnecessary and high-risk pancreatic surgeries, more precise diagnostic techniques are necessary. A promising approach involves integrating existing data, such as clinical features, cyst morphology, and data from cyst fluid analysis, with confocal endomicroscopy and radiomics to enhance the prediction of advanced neoplasms in PCLs. Artificial intelligence and machine learning modalities can play a crucial role in achieving this goal. In this review, we explore current and future techniques to leverage these advanced technologies to improve diagnostic accuracy in the context of PCLs.
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In this paper, a robust illuminant estimation algorithm for color constancy is proposed. Considering the drawback of the well-known max-RGB algorithm, which regards only pixels with the maximum image intensities, we explore the representative pixels from an image for illuminant estimation: The representative pixels are determined via the intensity bounds corresponding to a certain percentage value in the normalized accumulative histograms. To achieve the suitable percentage, an iterative algorithm is presented by simultaneously neutralizing the chromaticity distribution and preventing overcorrection. The experimental results on the benchmark databases provided by Simon Fraser University and Microsoft Research Cambridge, as well as several web images, demonstrate the effectiveness of our approach.
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Few-shot learning (FSL) aims to generate a classifier using limited labeled examples. Many existing works take the meta-learning approach, constructing a few-shot learner (a meta-model) that can learn from few-shot examples to generate a classifier. The performance is measured by how well the resulting classifiers classify the test (\ie, query) examples of those tasks. In this paper, we point out two potential weaknesses of this approach. First, the sampled query examples may not provide sufficient supervision for meta-training the few-shot learner. Second, the effectiveness of meta-learning diminishes sharply with the increasing number of shots. We propose a novel meta-training objective for the few-shot learner, which is to encourage the few-shot learner to generate classifiers that perform like strong classifiers. Concretely, we associate each sampled few-shot task with a strong classifier, which is trained with ample labeled examples. The strong classifiers can be seen as the target classifiers that we hope the few-shot learner to generate given few-shot examples, and we use the strong classifiers to supervise the few-shot learner. We validate our approach in combinations with many representative meta-learning methods. More importantly, with our approach, meta-learning based FSL methods can consistently outperform non-meta-learning based methods at different numbers of shots.
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OBJECTIVES: For population databases, multivariable regressions are established analytical standards. The utilization of machine learning (ML) in population databases is novel. We compared conventional statistical methods and ML for predicting mortality in biliary acute pancreatitis (biliary AP). METHODS: Using the Nationwide Readmission Database (2010-2014), we identified patients (age ≥18 years) with admissions for biliary AP. These data were randomly divided into a training (70%) and test set (30%), stratified by the outcome of mortality. The accuracy of ML and logistic regression models in predicting mortality was compared using 3 different assessments. RESULTS: Among 97,027 hospitalizations for biliary AP, mortality rate was 0.97% (n = 944). Predictors of mortality included severe AP, sepsis, increasing age, and nonperformance of cholecystectomy. Assessment metrics for predicting the outcome of mortality, the scaled Brier score (odds ratio [OR], 0.24; 95% confidence interval [CI], 0.16-0.33 vs 0.18; 95% CI, 0.09-0.27), F-measure (OR, 43.4; 95% CI, 38.3-48.6 vs 40.6; 95% CI, 35.7-45.5), and the area under the receiver operating characteristic (OR, 0.96; 95% CI, 0.94-0.97 vs 0.95; 95% CI, 0.94-0.96) were comparable between the ML and logistic regression models, respectively. CONCLUSIONS: For population databases, traditional multivariable analysis is noninferior to ML-based algorithms in predictive modeling of hospital outcomes for biliary AP.
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Pancreatite , Adolescente , Humanos , Doença Aguda , Mortalidade Hospitalar , Modelos Logísticos , Aprendizado de Máquina , Pancreatite/epidemiologia , Estudos RetrospectivosRESUMO
The rate of incidentally detected pancreatic cystic lesions (PCLs) has increased over the past decade and was recently reported at 8%. These lesions pose a unique challenge, as each subtype of PCL carries a different risk of malignant transformation, ranging from 0% (pancreatic pseudocyst) to 34-68% (main duct intraductal papillary mucinous neoplasm). It is imperative to correctly risk-stratify the malignant potential of these lesions in order to provide the correct care course for the patient, ranging from monitoring to surgical intervention. Even with the multiplicity of guidelines (i.e., the American Gastroenterology Association guidelines and Fukuoka/International Consensus guidelines) and multitude of diagnostic information, risk stratification of PCLs falls short. Studies have reported that 25-64% of patients undergoing PCL resection have pancreatic cysts with no malignant potential, and up to 78% of mucin-producing cysts resected harbor no malignant potential on pathological evaluation. Clinicians are now incorporating artificial intelligence technology to aid in the management of these difficult lesions. This review article focuses on advancements in artificial intelligence within digital pathomics, radiomics, and genomics as they apply to the diagnosis and risk stratification of PCLs.
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Accurately segmenting teeth and identifying the corresponding anatomical landmarks on dental mesh models are essential in computer-aided orthodontic treatment. Manually performing these two tasks is time-consuming, tedious, and, more importantly, highly dependent on orthodontists' experiences due to the abnormality and large-scale variance of patients' teeth. Some machine learning-based methods have been designed and applied in the orthodontic field to automatically segment dental meshes (e.g., intraoral scans). In contrast, the number of studies on tooth landmark localization is still limited. This paper proposes a two-stage framework based on mesh deep learning (called TS-MDL) for joint tooth labeling and landmark identification on raw intraoral scans. Our TS-MDL first adopts an end-to-end iMeshSegNet method (i.e., a variant of the existing MeshSegNet with both improved accuracy and efficiency) to label each tooth on the downsampled scan. Guided by the segmentation outputs, our TS-MDL further selects each tooth's region of interest (ROI) on the original mesh to construct a light-weight variant of the pioneering PointNet (i.e., PointNet-Reg) for regressing the corresponding landmark heatmaps. Our TS-MDL was evaluated on a real-clinical dataset, showing promising segmentation and localization performance. Specifically, iMeshSegNet in the first stage of TS-MDL reached an averaged Dice similarity coefficient (DSC) at 0.964±0.054 , significantly outperforming the original MeshSegNet. In the second stage, PointNet-Reg achieved a mean absolute error (MAE) of 0.597±0.761 mm in distances between the prediction and ground truth for 66 landmarks, which is superior compared with other networks for landmark detection. All these results suggest the potential usage of our TS-MDL in orthodontics.
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Aprendizado Profundo , Dente , Humanos , Processamento de Imagem Assistida por Computador/métodos , Telas Cirúrgicas , Dente/diagnóstico por imagem , Aprendizado de MáquinaRESUMO
Analytical tools that have spatial resolution at the nanometre scale are indispensable for the life and physical sciences. It is desirable that these tools also permit elemental and chemical identification on a scale of 10 nm or less, with large penetration depths. A variety of techniques in X-ray imaging are currently being developed that may provide these combined capabilities. Here we report the achievement of sub-15-nm spatial resolution with a soft X-ray microscope--and a clear path to below 10 nm--using an overlay technique for zone plate fabrication. The microscope covers a spectral range from a photon energy of 250 eV (approximately 5 nm wavelength) to 1.8 keV (approximately 0.7 nm), so that primary K and L atomic resonances of elements such as C, N, O, Al, Ti, Fe, Co and Ni can be probed. This X-ray microscopy technique is therefore suitable for a wide range of studies: biological imaging in the water window; studies of wet environmental samples; studies of magnetic nanostructures with both elemental and spin-orbit sensitivity; studies that require viewing through thin windows, coatings or substrates (such as buried electronic devices in a silicon chip); and three-dimensional imaging of cryogenically fixed biological cells.