6 research outputs found
UWAT-GAN: Fundus Fluorescein Angiography Synthesis via Ultra-wide-angle Transformation Multi-scale GAN
Fundus photography is an essential examination for clinical and differential
diagnosis of fundus diseases. Recently, Ultra-Wide-angle Fundus (UWF)
techniques, UWF Fluorescein Angiography (UWF-FA) and UWF Scanning Laser
Ophthalmoscopy (UWF-SLO) have been gradually put into use. However, Fluorescein
Angiography (FA) and UWF-FA require injecting sodium fluorescein which may have
detrimental influences. To avoid negative impacts, cross-modality medical image
generation algorithms have been proposed. Nevertheless, current methods in
fundus imaging could not produce high-resolution images and are unable to
capture tiny vascular lesion areas. This paper proposes a novel conditional
generative adversarial network (UWAT-GAN) to synthesize UWF-FA from UWF-SLO.
Using multi-scale generators and a fusion module patch to better extract global
and local information, our model can generate high-resolution images. Moreover,
an attention transmit module is proposed to help the decoder learn effectively.
Besides, a supervised approach is used to train the network using multiple new
weighted losses on different scales of data. Experiments on an in-house UWF
image dataset demonstrate the superiority of the UWAT-GAN over the
state-of-the-art methods. The source code is available at:
https://github.com/Tinysqua/UWAT-GAN.Comment: 26th International Conference on Medical Image Computing and Computer
Assisted Interventio
TTMFN: Two-stream Transformer-based Multimodal Fusion Network for Survival Prediction
Survival prediction plays a crucial role in assisting clinicians with the
development of cancer treatment protocols. Recent evidence shows that
multimodal data can help in the diagnosis of cancer disease and improve
survival prediction. Currently, deep learning-based approaches have experienced
increasing success in survival prediction by integrating pathological images
and gene expression data. However, most existing approaches overlook the
intra-modality latent information and the complex inter-modality correlations.
Furthermore, existing modalities do not fully exploit the immense
representational capabilities of neural networks for feature aggregation and
disregard the importance of relationships between features. Therefore, it is
highly recommended to address these issues in order to enhance the prediction
performance by proposing a novel deep learning-based method. We propose a novel
framework named Two-stream Transformer-based Multimodal Fusion Network for
survival prediction (TTMFN), which integrates pathological images and gene
expression data. In TTMFN, we present a two-stream multimodal co-attention
transformer module to take full advantage of the complex relationships between
different modalities and the potential connections within the modalities.
Additionally, we develop a multi-head attention pooling approach to effectively
aggregate the feature representations of the two modalities. The experiment
results on four datasets from The Cancer Genome Atlas demonstrate that TTMFN
can achieve the best performance or competitive results compared to the
state-of-the-art methods in predicting the overall survival of patients
A Comprehensive Curation Shows the Dynamic Evolutionary Patterns of Prokaryotic CRISPRs
Motivation. Clustered regularly interspaced short palindromic repeat (CRISPR) is a genetic element with active regulation roles for foreign invasive genes in the prokaryotic genomes and has been engineered to work with the CRISPR-associated sequence (Cas) gene Cas9 as one of the modern genome editing technologies. Due to inconsistent definitions, the existing CRISPR detection programs seem to have missed some weak CRISPR signals. Results. This study manually curates all the currently annotated CRISPR elements in the prokaryotic genomes and proposes 95 updates to the annotations. A new definition is proposed to cover all the CRISPRs. The comprehensive comparison of CRISPR numbers on the taxonomic levels of both domains and genus shows high variations for closely related species even in the same genus. The detailed investigation of how CRISPRs are evolutionarily manipulated in the 8 completely sequenced species in the genus Thermoanaerobacter demonstrates that transposons act as a frequent tool for splitting long CRISPRs into shorter ones along a long evolutionary history
MUSTv2: An Improved De Novo Detection Program for Recently Active Miniature Inverted Repeat Transposable Elements (MITEs)
Miniature inverted repeat transposable element (MITE) is a short transposable element, carrying no protein-coding regions. However, its high proliferation rate and sequence-specific insertion preference renders it as a good genetic tool for both natural evolution and experimental insertion mutagenesis. Recently active MITE copies are those with clear signals of Terminal Inverted Repeats (TIRs) and Direct Repeats (DRs), and are recently translocated into their current sites. Their proliferation ability renders them good candidates for the investigation of genomic evolution
Additional file 1: Figure S1. of McTwo: a two-step feature selection algorithm based on maximal information coefficient
Comparison of the binary classification accuracy Acc between the two algorithms McTwo and McOne. Figure S2. Comparison of the binary classification accuracy Acc among the four algorithms, McTwo, CFS, PAM and RRF. Figure S3. Comparison of the binary classification accuracy Acc among the four algorithms, McTwo, TRank, WRank and RCORank. Table S1. Comparison of the binary classification accuracy Acc between the two algorithms McTwo and McOne. Table S2. Comparison of McTwo with the three individual ranking algorithms. Table S3. Statistical significance of the comparison triplets of McTwo with the other feature selection algorithms. (PDF 931Â kb