20 research outputs found
Hierarchical Cross-modal Transformer for RGB-D Salient Object Detection
Most of existing RGB-D salient object detection (SOD) methods follow the
CNN-based paradigm, which is unable to model long-range dependencies across
space and modalities due to the natural locality of CNNs. Here we propose the
Hierarchical Cross-modal Transformer (HCT), a new multi-modal transformer, to
tackle this problem. Unlike previous multi-modal transformers that directly
connecting all patches from two modalities, we explore the cross-modal
complementarity hierarchically to respect the modality gap and spatial
discrepancy in unaligned regions. Specifically, we propose to use intra-modal
self-attention to explore complementary global contexts, and measure
spatial-aligned inter-modal attention locally to capture cross-modal
correlations. In addition, we present a Feature Pyramid module for Transformer
(FPT) to boost informative cross-scale integration as well as a
consistency-complementarity module to disentangle the multi-modal integration
path and improve the fusion adaptivity. Comprehensive experiments on a large
variety of public datasets verify the efficacy of our designs and the
consistent improvement over state-of-the-art models.Comment: 10 pages, 10 figure
YOLOrtho -- A Unified Framework for Teeth Enumeration and Dental Disease Detection
Detecting dental diseases through panoramic X-rays images is a standard
procedure for dentists. Normally, a dentist need to identify diseases and find
the infected teeth. While numerous machine learning models adopting this
two-step procedure have been developed, there has not been an end-to-end model
that can identify teeth and their associated diseases at the same time. To fill
the gap, we develop YOLOrtho, a unified framework for teeth enumeration and
dental disease detection. We develop our model on Dentex Challenge 2023 data,
which consists of three distinct types of annotated data. The first part is
labeled with quadrant, and the second part is labeled with quadrant and
enumeration and the third part is labeled with quadrant, enumeration and
disease. To further improve detection, we make use of Tufts Dental public
dataset. To fully utilize the data and learn both teeth detection and disease
identification simultaneously, we formulate diseases as attributes attached to
their corresponding teeth. Due to the nature of position relation in teeth
enumeration, We replace convolution layer with CoordConv in our model to
provide more position information for the model. We also adjust the model
architecture and insert one more upsampling layer in FPN in favor of large
object detection. Finally, we propose a post-process strategy for teeth layout
that corrects teeth enumeration based on linear sum assignment. Results from
experiments show that our model exceeds large Diffusion-based model
Futher increase of obesity prevalence in Chinese children and adolescents - cross-sectional data of two consecutive samples from the city of Shanghai from 2003 to 2008
RNF43 is associated with genomic features and clinical outcome in BRAF mutant colorectal cancer
BackgroundColorectal cancer (CRC) patients with BRAF mutation have very poor prognosis. It is urgent to search for prognostic factors of BRAF mutant CRC. RNF43 is a ENF ubiquitin ligase of Wnt signaling. Mutation of RNF43 has been observed frequently in various types of human cancers. However, few studies have evaluated the role of RNF43 in CRC. The present study aimed to explore the impact of RNF43 mutations on molecular characteristics and prognosis in BRAF mutant CRC.MethodsSamples of 261 CRC patients with BRAF mutation were retrospectively analyzed. Tumor tissue and matched peripheral blood samples were collected and subjected to targeted sequencing with a panel of 1021 cancer-related genes. The association of molecular characteristics and survival in patients were then analyzed. 358 CRC patients with BRAF mutation from the cBioPortal dataset were used for further confirmation.ResultsThis study was inspired by a CRC patient with BRAF V600E and RNF43 co-mutation, who achieved a best remission of 70% and a progression free survival (PFS) of 13 months. Genomic analysis indicated that RNF43 mutation affected the genomic characteristics of patients with BRAF mutation, including microsatellite instability (MSI), tumor mutation burden (TMB) and the proportion of common gene mutations. Survival analysis showed that RNF43 mutation was a predictive biomarker for better PFS and OS in BRAF mutant CRC.ConclusionCollectively, we identified that RNF43 mutations were correlated with favorable genomic features, resulting in a better clinical outcome for BRAF mutant CRC patients
SNPS in the exons of toll-like receptors are associated with susceptibility to type 1 diabetes in Chinese population
The Role of Synthesis Parameters on Crystallization and Grain Size in Hybrid Halide Perovskite Solar Cells
Antisolvent
method has been widely applied to fabricate hybrid halide
perovskite solar cells. However, the quality of perovskite film is
unable to effectively control for the excessively fast crystallization.
In this work, we investigated the effect of precursor concentration
and spinning speed as well as additive CH<sub>3</sub>NH<sub>3</sub>Cl on quality perovskite film and thus the performance solar cells.
The growth of crystal toward the orientation parallel to the direction
of facet (002)/(004) slows down as the concentration and spinning
speed increases. The lattice constants points out the expansion of
crystal size in the direction along the <i>c</i>-axis of
around 0.04 Ã… due to the size difference between I and Cl ions.
A vertical growth mechanism was proposed to elaborate the crystallite
growth of CH<sub>3</sub>NH<sub>3</sub>PbI<sub>3</sub>(Cl) perovskite
film. Consequently, the perovskite solar cell efficiency impressively
increases from ∼12% to ∼16% along enlarging the crystal
size. Various advanced characterization techniques including ultrafast
spectroscopy and electronic impedance spectroscopy elucidated that
an optimal film with large grain size and less defect states could
result in the enhancement of charge transport and collection efficiency