189 research outputs found
Interpretable and Efficient Beamforming-Based Deep Learning for Single Snapshot DOA Estimation
We introduce an interpretable deep learning approach for direction of arrival
(DOA) estimation with a single snapshot. Classical subspace-based methods like
MUSIC and ESPRIT use spatial smoothing on uniform linear arrays for single
snapshot DOA estimation but face drawbacks in reduced array aperture and
inapplicability to sparse arrays. Single-snapshot methods such as compressive
sensing and iterative adaptation approach (IAA) encounter challenges with high
computational costs and slow convergence, hampering real-time use. Recent deep
learning DOA methods offer promising accuracy and speed. However, the practical
deployment of deep networks is hindered by their black-box nature. To address
this, we propose a deep-MPDR network translating minimum power distortionless
response (MPDR)-type beamformer into deep learning, enhancing generalization
and efficiency. Comprehensive experiments conducted using both simulated and
real-world datasets substantiate its dominance in terms of inference time and
accuracy in comparison to conventional methods. Moreover, it excels in terms of
efficiency, generalizability, and interpretability when contrasted with other
deep learning DOA estimation networks.Comment: 10 pages, 10 figure
M cells are involved in pathogenesis of human contact lens-associated giant papillary conjunctivitis
INTRODUCTION: The objective was to study the pathogenesis of contact lens-associated giant papillary conjunctivitis (CL-GPC). MATERIALS AND METHODS: Twenty-one biopsies of conjunctival giant papillae were obtained from soft contact lens wearers. The tissues were fixed in 4% paraformaldehyde and embedded in paraffin. Sections of 5 ”m thickness were used for studies of histology and immunohistochemistry of pan-B and pan-T cell distributions. RESULTS: Conjunctival epitheliums on the top of conjunctiva-associated lymphoid tissue typically lacked goblet cells. Lymphocytes from underlying lymphoid follicle were pressed into intra-epithelial âpocketsâ formed through epithelial invagination. Under the follicle-associated epithelium, pan-B cells were mostly gathered in the central folliclar area and intraepithelial pockets, while CD3-positive T cells were predominantly distributed in parafolliclar region, but only a few in the intraepithelial pockets. CONCLUSIONS: Membranous epithelial cells (M cells) play a key role in the pathogenesis of CL-GPC for the binding and translocation of antigen and pathogen
SCPAT-GAN: Structural Constrained and Pathology Aware Convolutional Transformer-GAN for Virtual Histology Staining of Human Coronary OCT images
There is a significant need for the generation of virtual histological
information from coronary optical coherence tomography (OCT) images to better
guide the treatment of coronary artery disease. However, existing methods
either require a large pixel-wisely paired training dataset or have limited
capability to map pathological regions. To address these issues, we proposed a
structural constrained, pathology aware, transformer generative adversarial
network, namely SCPAT-GAN, to generate virtual stained H&E histology from OCT
images. The proposed SCPAT-GAN advances existing methods via a novel design to
impose pathological guidance on structural layers using transformer-based
network.Comment: 9 pages, 4 figure
Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network
Optical coherence tomography (OCT) has stimulated a wide range of medical
image-based diagnosis and treatment in fields such as cardiology and
ophthalmology. Such applications can be further facilitated by deep
learning-based super-resolution technology, which improves the capability of
resolving morphological structures. However, existing deep learning-based
method only focuses on spatial distribution and disregard frequency fidelity in
image reconstruction, leading to a frequency bias. To overcome this limitation,
we propose a frequency-aware super-resolution framework that integrates three
critical frequency-based modules (i.e., frequency transformation, frequency
skip connection, and frequency alignment) and frequency-based loss function
into a conditional generative adversarial network (cGAN). We conducted a
large-scale quantitative study from an existing coronary OCT dataset to
demonstrate the superiority of our proposed framework over existing deep
learning frameworks. In addition, we confirmed the generalizability of our
framework by applying it to fish corneal images and rat retinal images,
demonstrating its capability to super-resolve morphological details in eye
imaging.Comment: 13 pages, 7 figures, submitted to Biomedical Optics Express special
issu
The acute toxicity of cypermethrin, emamectin benzoate and imidacloprid on red swamp crayfish (<em>Procambarus clarkia</em>)
Pesticide contamination is commonly found as a mixture of different pesticides rather than individual compounds. However, the regulatory risk evaluation is mostly based on the effects of individual pesticides. In the present study, we aimed to investigate the individual and combined toxicities of cypermethrin (CYP) with emamectin benzoate (EMB) and imidacloprid (IMI) to crayfish using acute indices and various sub-lethal endpoints. Semi-static bioassay procedures were followed in the experiment. The 24, 48, and 72 h LC~50~ values (with 95% confidence limits) of CYP for crayfish were calculated as 0.141, 0.137, and 0.135 ug/ml, respectively, which were higher than those of IMI (75.813, 72.345, 70.568 ug/ml) and EMB (34.581, 27.930, 22.298 ug/ml). Pesticide mixtures of CYP and EMB displayed a synergistic response to crayfish; the LC50 was 0.053, 0.050, and 0.048 ug/ml, which was lower than when only CYP was present. Pesticide mixtures of CYP and EMB were found to be highly toxic to crayfish. At the physiological level, both individuals and mixtures of pesticides caused severe injury to the internal organs of crayfish. Taken together, the synergistic effects indicated that it was highly important to include joint toxicity studies when assessing the risk of pesticides
Push the Boundary of SAM: A Pseudo-label Correction Framework for Medical Segmentation
Segment anything model (SAM) has emerged as the leading approach for
zero-shot learning in segmentation, offering the advantage of avoiding
pixel-wise annotation. It is particularly appealing in medical image
segmentation where annotation is laborious and expertise-demanding. However,
the direct application of SAM often yields inferior results compared to
conventional fully supervised segmentation networks. While using SAM generated
pseudo label could also benefit the training of fully supervised segmentation,
the performance is limited by the quality of pseudo labels. In this paper, we
propose a novel label corruption to push the boundary of SAM-based
segmentation. Our model utilizes a novel noise detection module to distinguish
between noisy labels from clean labels. This enables us to correct the noisy
labels using an uncertainty-based self-correction module, thereby enriching the
clean training set. Finally, we retrain the network with updated labels to
optimize its weights for future predictions. One key advantage of our model is
its ability to train deep networks using SAM-generated pseudo labels without
relying on a subset of expert-level annotations. We demonstrate the
effectiveness of our proposed model on both X-ray and lung CT datasets,
indicating its ability to improve segmentation accuracy and outperform baseline
methods in label correction
Deep Learning based 3D Segmentation: A Survey
3D object segmentation is a fundamental and challenging problem in computer
vision with applications in autonomous driving, robotics, augmented reality and
medical image analysis. It has received significant attention from the computer
vision, graphics and machine learning communities. Traditionally, 3D
segmentation was performed with hand-crafted features and engineered methods
which failed to achieve acceptable accuracy and could not generalize to
large-scale data. Driven by their great success in 2D computer vision, deep
learning techniques have recently become the tool of choice for 3D segmentation
tasks as well. This has led to an influx of a large number of methods in the
literature that have been evaluated on different benchmark datasets. This paper
provides a comprehensive survey of recent progress in deep learning based 3D
segmentation covering over 150 papers. It summarizes the most commonly used
pipelines, discusses their highlights and shortcomings, and analyzes the
competitive results of these segmentation methods. Based on the analysis, it
also provides promising research directions for the future.Comment: Under review of ACM Computing Surveys, 36 pages, 10 tables, 9 figure
The development of meibomian glands in mice
PurposeThe purpose of this study was to characterize the natural history of meibomian gland morphogenesis in the mouse.MethodsEmbryonic (E) and post natal (P) C57Bl/6 mouse pups were obtained at E18.5, P0, P1, P3, P5, P8, P15, and P60. Eyelids were fixed and processed for en bloc staining with Phalloidin/DAPI to identify gland morphogenesis, or frozen for immunohistochemistry staining with Oil red O (ORO) to identify lipid and antibodies specific against peroxisome proliferator-activated receptor gamma (PPARÎł) to identify meibocyte differentiation. Samples were then evaluated using a Zeiss 510 Meta laser scanning confocal microscope or Nikon epi-fluorescent microscope. Tissues from adult mice (2 month-old) were also collected for western blotting.ResultsMeibomian gland morphogenesis was first detected at E18.5 with the formation of an epithelial placode within the fused eyelid margin. Invagination of the epithelium into the eyelid was detected at P0. From P1 to P3 there was continued extension of the epithelium into the eyelid. ORO and PPARÎł staining was first detected at P3, localized to the central core of the epithelial cord thus forming the presumptive ductal lumen. Ductal branching was first detected at P5 associated with acinar differentiation identified by ORO and PPARÎł staining. Adult meibomian glands were observed by P15. Western blotting of meibomian gland proteins identified a 50 kDa and a 72 kDa band that stained with antibodies specific to PPARÎł.ConclusionsWe have demonstrated that meibomian gland development bears distinct similarities to hair development with the formation of an epithelial placode and expression of PPARÎł co-incident with lipid synthesis and meibocyte differentiation
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