Miracle-dz/UniFAR-FTUs huggingface dataset:
Switchable positioning of plate-like inclusions in lipid membranes: Elastically mediated interactions of planar colloids in 2D fluids

Mechanical stimuli direct reversible intramembrane positioning of flat rigid microdomains on scales up to tens of micrometers. We demonstrate how manipulating curvature in an elastic fluid lamella enables the reversible relative positioning of flat, rigid, plate-like micrometer-scale inclusions, with spacings from about a micrometer to tens of micrometers. In an experimental model comprising giant unilamellar vesicles containing solid domain pairs coexisting in a fluid membrane, we adjusted vesicle inflation to manipulate membrane curvature and mapped the interdomain separation. A two-dimensional model of the pair potential predicts the salient experimental observations and reveals both attractions and repulsions, producing a potential minimum entirely a result of the solid domain rigidity and bending energy in the fluid membrane. The impact of vesicle inflation on domain separation in vesicles containing two solid domains was qualitatively consistent with observations in vesicles containing many domains. The behavior differs qualitatively from the pure repulsions between fluid membrane domains or interactions between nanoscopic inclusions whose repulsive or attractive character is not switchable.
Respiratory impedance measured using impulse oscillometry in a healthy urban population

This study derives normative prediction equations for respiratory impedance in a healthy asymptomatic urban population using an impulse oscillation system (IOS). In addition, this study uses body mass index (BMI) in the equations to describe the effect of obesity on respiratory impedance. Data from an urban population comprising 472 healthy asymptomatic subjects that resided or worked in lower Manhattan, New York City were retrospectively analysed. This population was the control group from a previously completed case–control study of the health effects of exposure to World Trade Center dust. Since all subjects underwent spirometry and oscillometry, these previously collected data allowed a unique opportunity to derive normative prediction equations for oscillometry in an urban, lifetime non-smoking, asymptomatic population without underlying respiratory disease. Normative prediction equations for men and women were successfully developed for a broad range of respiratory oscillometry variables with narrow confidence bands. Models that used BMI as an independent predictor of oscillometry variables (in addition to age and height) demonstrated equivalent or better fit when compared with models that used weight. With increasing BMI, resistance and reactance increased compatible with lung and airway compression from mass loading. This study represents the largest cohort of healthy urban subjects assessed with an IOS device. Normative prediction equations were derived that should facilitate application of IOS in the clinical setting. In addition, the data suggest that modelling of lung function may be best performed using height and BMI as independent variables rather than the traditional approach of using height and weight. Prediction equations for respiratory impedance were derived in an urban cohort incorporating the effects of mass loading from obesity. Urban exposures had minimal effect on impedance allowing application of the equations to a broad range of populations. https://bit.ly/3a3zZvd
Architectural distortion detection in digital breast tomosynthesis with adaptive receptive field and adaptive convolution kernel shape

Architectural distortion (AD) is one of the breast abnormal signs in medical imaging and it is hard to be detected in clinic because of its subtle appearance and similar intensity with surrounding tissues. We previously developed a deep-learning-based model for AD detection in digital breast tomosynthesis (DBT). However, for atypical ADs, the model’s detection performance was not good enough because atypical ADs do not have a radial pattern, which is the main characteristic of AD. Considering that radiologists always take surrounding tissues’ information as reference to locate atypical ADs, an ideal model should not only adapt to the different shape of atypical ADs, but also have a large receptive field. In this study, deformable convolution kernel was employed to establish a novel deep-learning-based AD detection model. A dataset of 265 DBT volumes including 64 typical ADs, 74 atypical ADs and 127 normal volumes were collected for model evaluation. Mean true positive fraction (MTPF) was used as figure-of-merit. The results of six-fold cross-validation showed that after involving deformable convolution, the MTPF improved from 0.53±0.04 to 0.56±0.04 (p=0.028) and the number of false positives (FPs) at 80% sensitivity reduced from 1.95 to 1.09. Especially for atypical AD, the MTPF improved from 0.45±0.05 to 0.51±0.04 (p=0.01) and the number of FPs at 80% sensitivity reduced from 4.79 to 1.51. These results showed that this model has potential to assist radiologists locate more suspicious ADs and improve their diagnosis efficiency.
Deep learning for the detection of landmarks in head CT images and automatic quality assessment

In order to alleviate the risk of radiation induced cataract in patients undergoing head CT examinations, the guidelines published by the American Association of Physicists in Medicine (AAPM) link the optimal scan angle to particular anatomic landmarks in the skull. In this paper, we investigated the use of a foveal fully-convolutional neural network (F-Net) for the segmentation-based detection of three head CT landmarks, with the final objective of an automatic scan quality control. Three individual networks were trained using ground-truth (GT) from three different readers to investigate the detection accuracy compared to each reader. The experiments were performed using 119 head CT scans and the three-fold cross-validation set up. For the evaluation, two performance measures were employed: the Euclidean distance between the detected landmarks and GT, and the distance of the detected landmarks to the plane generated from the GT landmark positions. For three readers, the median values of the Euclidean and point-to-plane distance obtained using F-Net were in the range of 1.3 - 2.8 mm and 0.3 - 0.8 mm, respectively. The presented method outperformed a previously published approach using image registration and achieved results comparable to the inter-observer variability between three readers. Further improvements were achieved by training a similar network which combined GT information from all readers.
Noise characteristics in low to high kVp domain translation with Deep Regression Network

In Computed Tomography (CT), problems like low to high dose image conversion, denoising and super-resolution using deep regression network have gained a lot of importance. This led to the study of multiple denoising approaches with these networks, as non-linear characteristics of the network often changes the noise pattern. Noise2Noise (N2N) and similar studies have shown the impact of these networks, where one can use noisy image pairs and exploit the uncorrelated nature of noise to generate denoised images. In this paper, we study the behaviour of regression network for a domain translation between one energy image to another energy and its impact on noise. Inter-kVp translation leads to change in attenuation values in CT image. A design of experiments is set up using CATSim phantom with different tissue types at varying levels of density and proportions of materials, for heart, soft tissue, fat, calcification and bone. The intent is to understand the impact of inter and intra-tissue dynamic ranges, as this network learns intensity translation of the image between 2 kVp levels and noise characteristics. The results demonstrate ability of regression network to change intensty values from low kVp image to high kVp image. It also shows impact on noise level in a tissue is proportional to (a) intra-tissue variability and (b) the desired mean shift of the tissue between two energy levels. Simultaneously, there is a marked change in the artifacts and resultant image quality, as expected, through the learning method.
Retinal phantom applicable to OCT, OCT-angiography, and fundus angiography

The retinal imaging system, such as optical coherence tomography (OCT), OCT-angiography (OCTA) and fluorescein angiography (FA), is the important subject for ophthalmic. The use of such equipment continues to increase and retinal phantoms have also been developed to evaluate and modify its performance and image quality. In this presentation, we will show methods and results of a retinal phantom that can evaluate the optical performance of OCT, OCTA and FA. We implemented superficial vascular networks and full retinal layers which has curvature. We could obtain cross-sectional OCT images and en-face OCTA images using lab-made OCT system. In addition, FA image could also be obtained through sodium fluorescein dye injection.
Dense-layer-based YOLO-v3 for detection and localization of colon perforations

Endoscopic submucosal dissection is a minimally invasive treatment for early gastric cancer. In endoscopic submucosal dissection, a physician directly removes the mucosa around the lesion under internal endoscopy by using the flush knife. However, the flush knife may accidentally pierce the colonic wall and generate a perforation on it. If physicians overlooking a small perforation, a patient may need emergency open surgery, since a perforation can easily cause peritonitis. For the prevention of overlooking of perforations, a computer-aided diagnosis system has a potential demand. We believe automatic perforation detection and localization function is very useful for the analysis of endoscopic submucosal dissection videos for the development of a computeraided diagnosis system. At current stage, the research of perforation detection and localization progress slowly, automatic image-based perforation detection is very challenge. Thus, we devote to the development of detection and localization of perforations in colonoscopic videos. In this paper, we proposed a supervised-learning method for perforations detection and localization in colonoscopic videos. This method uses dense layers in YOLO-v3 instead of residual units, and a combination of binary cross entropy and generalized intersection over union loss as the loss function in the training process. This method achieved 0.854 accuracy, 0.850 AUC score and 0.884 mean average precision for perforation detection and localization, respectively, as an initial study
Enhancing Z-resolution in CT volumes with deep residual learning

CT systems with large detector size suffer from lower z-resolution leading to pixelated images and inability to detect small structures thus adversely impacting the diagnosis and screening. Overlap reconstruction can partially reduce the stair-step artifacts but does not improve the effect of wider slice sensitivity profile (SSP) and thus continues to have reduced visibility of smaller structures. In this work, we propose a supervised deep learning method for z-resolution enhancement such that (a) the effective SSP of resulting image is reduced, (b) quantitative values of tissue (CT numbers) and tissue-contrast are preserved; (c) very limited noise enhancement and (d) improved tissue interface in bone/soft tissue. The proposed method devises a super resolution (SURE) network which is trained to map the low resolution (LR) slices to the corresponding high resolution (HR) slices. A 2D network is trained with sagittal and coronal slices with the LR-HR pair sets. The training is performed using ground truth HR slices obtained from high end systems, and the corresponding LR slices are synthesized by either using retro reconstruction with higher slice thickness and spacing or through averaging of slices in z-direction from HR images. The network is trained with both these types of images with helical acquisition volumes from a range of scanners. Qualitative and quantitative analysis is done on the predicted HR images and compared with the original HR images. FWHM for SSP of the predicted HR images reduced from ~0.98 to ~0.73, when the target was 0.64, thus improving the real z-resolution. HU distribution of different tissue types also showed stability in terms of mean value. Noise measured through standard deviation was slightly higher than the LR image but lower than that of original HR images. PSNR also showed consistent improvement on all the cases across 3 different systems.
Transfer learning methods for classification of COVID-19 chest x-ray images

An important point-of-care diagnostic technology for COVID-19 is x-ray imaging of the lungs. Here we present a novel deep learning training method which combines both supervised and reinforcement learning methodologies which allows transfer learning in a convolutional neural network (CNN). The method integrated hill-climbing techniques and stochastic gradient descent with momentum to train the CNN architectures without overfitting on small datasets. The model was trained using the Kaggle COVID-19 Chest Radiography dataset. The dataset consists of 219 COVID-19 positive images, 1341 normal images, and 1345 viral pneumonia images. Since training of a CNN can be affected by bias and depends on the limitations of available computing power, the data set was reduced to 219 images for each class. From each of the classes, 150 random images were used for training the CNN algorithm and the model was tested with 69 independent images. Transfer training was done on three models, namely, VGG-19, DenseNet-201, and NASNet. The DenseNet-201 architecture performed the best in terms of accuracy achieving an accuracy of 96.1%. The VGG-19 and DenseNet-201 had sensitivity of 91.3 % while NASnet had a slightly higher sensitivity of 92.8%. This shows that we can have high confidence of the classification results achieved by these models. These results show that deep learning methodologies can be used for identifying COVID-19 patients quickly and accurately.
Anatomy recognition in CT images of head and neck region via precision atlases

Multi-atlas segmentation methods will benefit from atlases covering the complete spectrum of population patterns, while the difficulties in generating such large enough datasets and the computation burden required in the segmentation procedure reduce its practicality in clinical application. In this work, we start from a viewpoint that different parts of the target object can be recognized by different atlases and propose a precision atlas selection strategy. By comparing regional similarity between target image and atlases, precision atlases are ranked and selected by the frequency of regional best match, which have no need to be globally similar to the target subject at either image-level or object-level, largely increasing the implicit patterns contained in the atlas set. In the proposed anatomy recognition method, atlas building is first achieved by all-totemplate registration, where the minimum spanning tree (MST) strategy is used to select a registration template from a subset of radiologically near-normal images. Then, a two-stage recognition process is conducted: in rough recognition, sub-image level similarity is calculated between the test image and each image of the whole atlas set, and only the atlas with the highest similarity contributes to the recognition map regionally; in refined recognition, the atlases with the highest frequencies of best match are selected as the precision atlases and are utilized to further increase the accuracy of boundary matching. The proposed method is demonstrated on 298 computed tomography (CT) images and 9 organs in the Head and Neck (HN) body region. Experimental results illustrate that our method is effective for organs with different segmentation challenge and samples with different image quality, where remarkable improvement in boundary interpretation is made by refined recognition and most objects achieve a localization error within 2 voxels.
Fluorescence lifetime imaging endomicroscopy-based ex-vivo lung cancer prediction using multi-scale concatenated-dilation convolutional neural networks

Deep learning technologies have been successfully applied to automatic diagnostics of ex-vivo lung cancer with fluorescence lifetime imaging endomicroscopy (FLIM). Recent advance in convolutional neural networks (CNNs) by splitting input features for multi-scale feature extraction as a feature-level aggregation, has achieved further improvement in visual recognition. However, due to the splitting, correlations among input features are no longer retained. To exploit the advantages of hierarchical multi-scale architectures, while maintaining the correlations as global information, we propose a novel architecture, namely multi-scale concatenated-dilation (MSCD) at a layer level. The MSCD performs multi-scale feature extraction on input features without the splitting. In addition, we substitute the Addition aggregation in the original hierarchical architecture with the Concatenation to retrieve more features. At the same time, we also introduce dilated convolutions to replace the linear convolutions to further enlarge the receptive field. We evaluate the performance of MSCD by integrating it into ResNet, on over 60,000 FLIM images collected from 14 patients, using a custom fiber-based FLIM system, with various user-specified configurations. Accuracy, precision, recall, and the area under the receiver operating characteristic curve are used as the metrics. We first demonstrate the superiority of our MSCD model over the backbone ResNet and other state-of-the-art CNNs in terms of higher scores with lower complexity over the metrics. Moreover, we empirically demonstrate the superiority of the Concatenation aggregation over the Addition on convolution and scale efficiency. Furthermore, we compare the MSCD with Res2Net to illustrate the advantages and disadvantages of feature-/layer-level multi-scale aggregation.
Two-stage meniscus segmentation framework integrating multiclass localization network and adversarial learning-based segmentation network in knee MR images

Meniscus segmentation from knee MR images is an essential step in finding the most suitable implant prototype for meniscus allograft transplantation using a 3D reconstruction model from the patient’s normal meniscus. However, the segmentation of the meniscus is challenging due to its thin shape, similar intensities with nearby structures such as cruciate and collateral ligaments in the knee MR images, large shape variations among patients, and inhomogeneous intensity within the meniscus. In addition, conventional deep convolutional neural network (DCNN)-based meniscus segmentation method mainly uses a pixel-wise objective function, and thus produces rather under-segmentation results due to the small shape of the meniscus or suffers from false positives that occur around the meniscus. To overcome these limitations, we propose a two-stage DCNN that combines a 2D U-Net-based meniscus localization network with a conditional generative adversarial network-based segmentation network. To efficiently localize the meniscus region to medial and lateral meniscus and feed the localized ROIs to the segmentation network, 2D U-Net-based DCNN segments knee MR images into six classes. To segment the medial and lateral meniscus while preventing under-segmentation due to intensity inhomogeneity within the meniscus and over-segmentation due to intensity similarity with surrounding structures, adversarial learning is performed repeatedly on the localized meniscus ROIs. The average DSC of the meniscus was 84.06% at the medial meniscus, and 83.19% at the lateral meniscus, respectively. These results showed that the proposed method prevented the meniscus from being over- and under-segmented by repeatedly judging and complementing the quality of segmentation results through adversarial learning.
Influencer Management Tools: Algorithmic Cultures, Brand Safety, and Bias

This article explores algorithmic influencer management tools, designed to support marketers in selecting influencers for advertising campaigns, based on categorizations such as brand suitability, “brand friendliness,” and “brand risk. ” I argue that, by approximating these values, tools reify existing social inequalities in influencer industries, particularly along the lines of sexuality, class, and race. They also deepen surveillance of influencer content by brand stakeholders, who are concerned that influencers will err and be “cancelled” (risking their investments in content). My critical framework synthesizes feminist critiques of ostensibly participatory influencer industries with close attention to critical algorithmic studies. This article provides an in-depth look at how brand risk and brand safety are predicted and measured using one tool, Peg. Through a “walk through” of this tool, underpinned by a wider industry ethnography, I demonstrate how value-laded algorithmic judgments map onto well-worn hierarchies of desirability and employability that originate from systemic bias along the lines of class, race, and gender.