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

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

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 CH₃NH₃Cl 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 Å due to the size difference between I and Cl ions. A vertical growth mechanism was proposed to elaborate the crystallite growth of CH₃NH₃PbI₃(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