340 research outputs found
Bilateral-Fuser: A Novel Multi-cue Fusion Architecture with Anatomical-aware Tokens for Fovea Localization
Accurate localization of fovea is one of the primary steps in analyzing
retinal diseases since it helps prevent irreversible vision loss. Although
current deep learning-based methods achieve better performance than traditional
methods, there still remain challenges such as utilizing anatomical landmarks
insufficiently, sensitivity to diseased retinal images and various image
conditions. In this paper, we propose a novel transformer-based architecture
(Bilateral-Fuser) for multi-cue fusion. This architecture explicitly
incorporates long-range connections and global features using retina and vessel
distributions for robust fovea localization. We introduce a spatial attention
mechanism in the dual-stream encoder for extracting and fusing self-learned
anatomical information. This design focuses more on features distributed along
blood vessels and significantly decreases computational costs by reducing token
numbers. Our comprehensive experiments show that the proposed architecture
achieves state-of-the-art performance on two public and one large-scale private
datasets. We also present that the Bilateral-Fuser is more robust on both
normal and diseased retina images and has better generalization capacity in
cross-dataset experiments.Comment: This paper is prepared for IEEE TRANSACTIONS ON MEDICAL IMAGIN
New Therapeutic Approaches for the Treatment of Rheumatoid Arthritis may Rise from the Cholinergic Anti-Inflammatory Pathway and Antinociceptive Pathway
Due to the complex etiology of rheumatoid arthritis (RA), it is difficult to be completely cured at the current stage although many approaches have been applied in clinics, especially the wide application of nonsteroidal anti-inflammatory drugs (NSAIDs) and disease-modifying antirheumatic drugs (DMARDs). New drug discovery and development via the recently discovered cholinergic anti-inflammatory and antinociceptive pathways should be promising. Based on the above, the nicotinic acetylcholine receptor agonists maintain the potential for the treatment of RA. Therefore, new therapeutic approaches may rise from these two newly discovered pathways. More preclinical experiments and clinical trials are required to confirm our viewpoint
Stellar Parameters of Main Sequence Turn-off Star Candidates Observed with the LAMOST and Kepler
Main sequence turn-off (MSTO) stars have advantages as indicators of Galactic
evolution since their ages could be robustly estimated from atmospheric
parameters. Hundreds of thousands of MSTO stars have been selected from the
LAMOST Galactic sur- vey to study the evolution of the Galaxy, and it is vital
to derive accurate stellar parameters. In this work, we select 150 MSTO star
candidates from the MSTO stars sample of Xiang that have asteroseismic
parameters and determine accurate stellar parameters for these stars combing
the asteroseismic parameters deduced from the Kepler photometry and atmospheric
parameters deduced from the LAMOST spectra.With this sample, we examine the age
deter- mination as well as the contamination rate of the MSTO stars sample. A
comparison of age between this work and Xiang shows a mean difference of 0.53
Gyr (7%) and a dispersion of 2.71 Gyr (28%). The results show that 79 of the
candidates are MSTO stars, while the others are contaminations from either main
sequence or sub-giant stars. The contamination rate for the oldest stars is
much higher than that for the younger stars. The main cause for the high
contamination rate is found to be the relatively large systematic bias in the
LAMOST surface gravity estimates.Comment: accepted by RA
Hydrogels enable negative pressure in water for efficient heat utilization and transfer
Metastable water in negative pressure can provide giant passive driving
pressure up to several megapascals for efficient evaporation-driven flow,
however, the practical applications with negative pressure are rare due to the
challenges of generating and maintaining large negative pressure. In this work,
we report a novel structure with thin hydrogel films as evaporation surfaces
and robust porous substrates as the supports, and obtain a high negative
pressure of -1.61 MPa through water evaporation. Molecular dynamics simulations
elucidate the essential role of strong interaction between water molecules and
polymer chains in generating the negative pressure. With such a large negative
pressure, we demonstrate a streaming potential generator that spontaneously
converts environmental energy into electricity and outputs a voltage of 1.06 V.
Moreover, we propose a "negative pressure heat pipe" for the first time, which
achieves a high heat transfer density of 11.2 kW cm-2 with a flow length of 1
m, showing the potential of negative pressure in efficient heat utilization and
transfer.Comment: 43 pages, 18 figure
Exploiting Spatial-temporal Data for Sleep Stage Classification via Hypergraph Learning
Sleep stage classification is crucial for detecting patients' health
conditions. Existing models, which mainly use Convolutional Neural Networks
(CNN) for modelling Euclidean data and Graph Convolution Networks (GNN) for
modelling non-Euclidean data, are unable to consider the heterogeneity and
interactivity of multimodal data as well as the spatial-temporal correlation
simultaneously, which hinders a further improvement of classification
performance. In this paper, we propose a dynamic learning framework STHL, which
introduces hypergraph to encode spatial-temporal data for sleep stage
classification. Hypergraphs can construct multi-modal/multi-type data instead
of using simple pairwise between two subjects. STHL creates spatial and
temporal hyperedges separately to build node correlations, then it conducts
type-specific hypergraph learning process to encode the attributes into the
embedding space. Extensive experiments show that our proposed STHL outperforms
the state-of-the-art models in sleep stage classification tasks
Bilateral-ViT For Robust Fovea Localization
The fovea is an important anatomical landmark of the retina. Detecting the
location of the fovea is essential for the analysis of many retinal diseases.
However, robust fovea localization remains a challenging problem, as the fovea
region often appears fuzzy, and retina diseases may further obscure its
appearance. This paper proposes a novel Vision Transformer (ViT) approach that
integrates information both inside and outside the fovea region to achieve
robust fovea localization. Our proposed network, named
Bilateral-Vision-Transformer (Bilateral-ViT), consists of two network branches:
a transformer-based main network branch for integrating global context across
the entire fundus image and a vessel branch for explicitly incorporating the
structure of blood vessels. The encoded features from both network branches are
subsequently merged with a customized Multi-scale Feature Fusion (MFF) module.
Our comprehensive experiments demonstrate that the proposed approach is
significantly more robust for diseased images and establishes the new state of
the arts using the Messidor and PALM datasets.Comment: This work has been accepted for oral presentation by ISBI202
Observation of Fungi, Bacteria, and Parasites in Clinical Skin Samples Using Scanning Electron Microscopy
This chapter highlights the description of the clinical manifestation and its pathogen and the host tissue damage observed under the Scanning Electron Microscope, which helps the clinician to understand the pathogen’s superstructure, the change of host subcell structure, and the laboratory workers to understand the clinical characteristics of pathogen-induced human skin lesions, to establish a two-way learning exchange database with vivid image
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