33 research outputs found
Domain Adaptation via Bidirectional Cross-Attention Transformer
Domain Adaptation (DA) aims to leverage the knowledge learned from a source
domain with ample labeled data to a target domain with unlabeled data only.
Most existing studies on DA contribute to learning domain-invariant feature
representations for both domains by minimizing the domain gap based on
convolution-based neural networks. Recently, vision transformers significantly
improved performance in multiple vision tasks. Built on vision transformers, in
this paper we propose a Bidirectional Cross-Attention Transformer (BCAT) for DA
with the aim to improve the performance. In the proposed BCAT, the attention
mechanism can extract implicit source and target mixup feature representations
to narrow the domain discrepancy. Specifically, in BCAT, we design a
weight-sharing quadruple-branch transformer with a bidirectional
cross-attention mechanism to learn domain-invariant feature representations.
Extensive experiments demonstrate that the proposed BCAT model achieves
superior performance on four benchmark datasets over existing state-of-the-art
DA methods that are based on convolutions or transformers
MMMNA-Net for Overall Survival Time Prediction of Brain Tumor Patients
Overall survival (OS) time is one of the most important evaluation indices
for gliomas situations. Multimodal Magnetic Resonance Imaging (MRI) scans play
an important role in the study of glioma prognosis OS time. Several deep
learning-based methods are proposed for the OS time prediction on multi-modal
MRI problems. However, these methods usually fuse multi-modal information at
the beginning or at the end of the deep learning networks and lack the fusion
of features from different scales. In addition, the fusion at the end of
networks always adapts global with global (eg. fully connected after
concatenation of global average pooling output) or local with local (eg.
bilinear pooling), which loses the information of local with global. In this
paper, we propose a novel method for multi-modal OS time prediction of brain
tumor patients, which contains an improved nonlocal features fusion module
introduced on different scales. Our method obtains a relative 8.76% improvement
over the current state-of-art method (0.6989 vs. 0.6426 on accuracy). Extensive
testing demonstrates that our method could adapt to situations with missing
modalities. The code is available at
https://github.com/TangWen920812/mmmna-net.Comment: Accepted EMBC 202
Transformer Lesion Tracker
Evaluating lesion progression and treatment response via longitudinal lesion
tracking plays a critical role in clinical practice. Automated approaches for
this task are motivated by prohibitive labor costs and time consumption when
lesion matching is done manually. Previous methods typically lack the
integration of local and global information. In this work, we propose a
transformer-based approach, termed Transformer Lesion Tracker (TLT).
Specifically, we design a Cross Attention-based Transformer (CAT) to capture
and combine both global and local information to enhance feature extraction. We
also develop a Registration-based Anatomical Attention Module (RAAM) to
introduce anatomical information to CAT so that it can focus on useful feature
knowledge. A Sparse Selection Strategy (SSS) is presented for selecting
features and reducing memory footprint in Transformer training. In addition, we
use a global regression to further improve model performance. We conduct
experiments on a public dataset to show the superiority of our method and find
that our model performance has improved the average Euclidean center error by
at least 14.3% (6mm vs. 7mm) compared with the state-of-the-art (SOTA). Code is
available at https://github.com/TangWen920812/TLT.Comment: Accepted MICCAI 202
RPLHR-CT Dataset and Transformer Baseline for Volumetric Super-Resolution from CT Scans
In clinical practice, anisotropic volumetric medical images with low
through-plane resolution are commonly used due to short acquisition time and
lower storage cost. Nevertheless, the coarse resolution may lead to
difficulties in medical diagnosis by either physicians or computer-aided
diagnosis algorithms. Deep learning-based volumetric super-resolution (SR)
methods are feasible ways to improve resolution, with convolutional neural
networks (CNN) at their core. Despite recent progress, these methods are
limited by inherent properties of convolution operators, which ignore content
relevance and cannot effectively model long-range dependencies. In addition,
most of the existing methods use pseudo-paired volumes for training and
evaluation, where pseudo low-resolution (LR) volumes are generated by a simple
degradation of their high-resolution (HR) counterparts. However, the domain gap
between pseudo- and real-LR volumes leads to the poor performance of these
methods in practice. In this paper, we build the first public real-paired
dataset RPLHR-CT as a benchmark for volumetric SR, and provide baseline results
by re-implementing four state-of-the-art CNN-based methods. Considering the
inherent shortcoming of CNN, we also propose a transformer volumetric
super-resolution network (TVSRN) based on attention mechanisms, dispensing with
convolutions entirely. This is the first research to use a pure transformer for
CT volumetric SR. The experimental results show that TVSRN significantly
outperforms all baselines on both PSNR and SSIM. Moreover, the TVSRN method
achieves a better trade-off between the image quality, the number of
parameters, and the running time. Data and code are available at
https://github.com/smilenaxx/RPLHR-CT.Comment: Accepted MICCAI 202
Submission to the Kidney Tumor Segmentation Challenge 2019
In this report, we present our method description of the submission to Kidney Tumor Segmentation Challenge 2019. In this challenge, the goal is to segment the kidney and kidney tumor from the CT scans. Our method is based on a common neural architecture U-Net variant, while we pay more attention to the preprocessing stage to better understand the kidney data and postprocessing stage to reduce false positives. The experiments and results show that our proposed methods increase the segmentation accuracy compared to the basic model
Transcriptome analysis of Deinagkistrodon acutus venomous gland focusing on cellular structure and functional aspects using expressed sequence tags
BACKGROUND: The snake venom gland is a specialized organ, which synthesizes and secretes the complex and abundant toxin proteins. Though gene expression in the snake venom gland has been extensively studied, the focus has been on the components of the venom. As far as the molecular mechanism of toxin secretion and metabolism is concerned, we still knew a little. Therefore, a fundamental question being arisen is what genes are expressed in the snake venom glands besides many toxin components? RESULTS: To examine extensively the transcripts expressed in the venom gland of Deinagkistrodon acutus and unveil the potential of its products on cellular structure and functional aspects, we generated 8696 expressed sequence tags (ESTs) from a non-normalized cDNA library. All ESTs were clustered into 3416 clusters, of which 40.16% of total ESTs belong to recognized toxin-coding sequences; 39.85% are similar to cellular transcripts; and 20.00% have no significant similarity to any known sequences. By analyzing cellular functional transcripts, we found high expression of some venom related genes and gland-specific genes, such as calglandulin EF-hand protein gene and protein disulfide isomerase gene. The transcripts of creatine kinase and NADH dehydrogenase were also identified at high level. Moreover, abundant cellular structural proteins similar to mammalian muscle tissues were also identified. The phylogenetic analysis of two snake venom toxin families of group III metalloproteinase and serine protease in suborder Colubroidea showed an early single recruitment event in the viperids evolutionary process. CONCLUSION: Gene cataloguing and profiling of the venom gland of Deinagkistrodon acutus is an essential requisite to provide molecular reagents for functional genomic studies needed for elucidating mechanisms of action of toxins and surveying physiological events taking place in the very specialized secretory tissue. So this study provides a first global view of the genetic programs for the venom gland of Deinagkistrodon acutus described so far and an insight into molecular mechanism of toxin secreting. All sequences data reported in this paper have been submitted into the public database [GenBank: DV556511-DV565206]
Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation
Open international challenges are becoming the de facto standard for
assessing computer vision and image analysis algorithms. In recent years, new
methods have extended the reach of pulmonary airway segmentation that is closer
to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation,
limited effort has been directed to quantitative comparison of newly emerged
algorithms driven by the maturity of deep learning based approaches and
clinical drive for resolving finer details of distal airways for early
intervention of pulmonary diseases. Thus far, public annotated datasets are
extremely limited, hindering the development of data-driven methods and
detailed performance evaluation of new algorithms. To provide a benchmark for
the medical imaging community, we organized the Multi-site, Multi-domain Airway
Tree Modeling (ATM'22), which was held as an official challenge event during
the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed
pulmonary airway annotation, including 500 CT scans (300 for training, 50 for
validation, and 150 for testing). The dataset was collected from different
sites and it further included a portion of noisy COVID-19 CTs with ground-glass
opacity and consolidation. Twenty-three teams participated in the entire phase
of the challenge and the algorithms for the top ten teams are reviewed in this
paper. Quantitative and qualitative results revealed that deep learning models
embedded with the topological continuity enhancement achieved superior
performance in general. ATM'22 challenge holds as an open-call design, the
training data and the gold standard evaluation are available upon successful
registration via its homepage.Comment: 32 pages, 16 figures. Homepage: https://atm22.grand-challenge.org/.
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