Multiple angle key points detection guided screening of unruptured intracranial aneurysms.

Early screening of unruptured intracranial aneurysms is critical for disease control so as to attract a lot of attention. However, the extremly small size of lesions and large variance of appearances pose difficulties in algorithm modeling. To tackle this challenge, the paper proposes a multiple angle key points detection guided screening method for localization and segmentation of intracranial aneurysms.

Mesh Regression Based Shape Enhancement Operator Designed for Organ Segmentation.

Organ delineation is critical for diagnosis and treatment planning so as to attract a lot of attention. Recently, neural network based methods yield accurate segmentation metrics like dice coefficient. However, they have to face the problem of indistinct boundaries since segmentation is usually modeled as a pixel classification task ignoring anatomical priors.

DFCL: Dual-pathway fusion contrastive learning for blind single-image visible watermark removal.

Digital image watermarking is a prevalent method for image copyright protection. As watermark embedding techniques evolve, research in copyright protection has increasingly extended into watermark removal. Recent advancements in deep learning and generative technologies have led to the development of public watermark removal solutions, addressing issues such as plagiarized, illegal, or outdated watermarks while driving significant improvements in robust watermark embedding.

Prior Knowledge-Guided Triple-Domain Transformer-GAN for Direct PET Reconstruction From Low-Count Sinograms.

To obtain high-quality positron emission tomography (PET) images while minimizing radiation exposure, numerous methods have been dedicated to acquiring standard-count PET (SPET) from low-count PET (LPET). However, current methods have failed to take full advantage of the different emphasized information from multiple domains, i.e.

Promoting fast MR imaging pipeline by full-stack AI.

Magnetic resonance imaging (MRI) is a widely used imaging modality in clinics for medical disease diagnosis, staging, and follow-up. Deep learning has been extensively used to accelerate k-space data acquisition, enhance MR image reconstruction, and automate tissue segmentation. However, these three tasks are usually treated as independent tasks and optimized for evaluation by radiologists, thus ignoring the strong dependencies among them; this may be suboptimal for downstream intelligent processing.

3D multi-modality Transformer-GAN for high-quality PET reconstruction.

Positron emission tomography (PET) scans can reveal abnormal metabolic activities of cells and provide favorable information for clinical patient diagnosis. Generally, standard-dose PET (SPET) images contain more diagnostic information than low-dose PET (LPET) images but higher-dose scans can also bring higher potential radiation risks. To reduce the radiation risk while acquiring high-quality PET images, in this paper, we propose a 3D multi-modality edge-aware Transformer-GAN for high-quality SPET reconstruction using the corresponding LPET images and T1 acquisitions from magnetic resonance imaging (T1-MRI).

SOUL-Net: A Sparse and Low-Rank Unrolling Network for Spectral CT Image Reconstruction.

Spectral computed tomography (CT) is an emerging technology, that generates a multienergy attenuation map for the interior of an object and extends the traditional image volume into a 4-D form. Compared with traditional CT based on energy-integrating detectors, spectral CT can make full use of spectral information, resulting in high resolution and providing accurate material quantification. Numerous model-based iterative reconstruction methods have been proposed for spectral CT reconstruction.

Unsupervised Domain Adaptive Dose Prediction via Cross-Attention Transformer and Target-Specific Knowledge Preservation.

Radiotherapy is one of the leading treatments for cancer. To accelerate the implementation of radiotherapy in clinic, various deep learning-based methods have been developed for automatic dose prediction. However, the effectiveness of these methods heavily relies on the availability of a substantial amount of data with labels, i.

TransDose: Transformer-based radiotherapy dose prediction from CT images guided by super-pixel-level GCN classification.

Radiotherapy is a mainstay treatment for cancer in clinic. An excellent radiotherapy treatment plan is always based on a high-quality dose distribution map which is produced by repeated manual trial-and-errors of experienced experts. To accelerate the radiotherapy planning process, many automatic dose distribution prediction methods have been proposed recently and achieved considerable fruits.

A Transformer-Embedded Multi-Task Model for Dose Distribution Prediction.

Radiation therapy is a fundamental cancer treatment in the clinic. However, to satisfy the clinical requirements, radiologists have to iteratively adjust the radiotherapy plan based on experience, causing it extremely subjective and time-consuming to obtain a clinically acceptable plan. To this end, we introduce a transformer-embedded multi-task dose prediction (TransMTDP) network to automatically predict the dose distribution in radiotherapy.

SFT-Net: A Network for Detecting Fatigue From EEG Signals by Combining 4D Feature Flow and Attention Mechanism.

Fatigued driving is a leading cause of traffic accidents, and accurately predicting driver fatigue can significantly reduce their occurrence. However, modern fatigue detection models based on neural networks often face challenges such as poor interpretability and insufficient input feature dimensions. This article proposes a novel Spatial-Frequency-Temporal Network (SFT-Net) method for detecting driver fatigue using electroencephalogram (EEG) data.

Facial Expression Recognition with Contrastive Learning and Uncertainty-Guided Relabeling.

Facial expression recognition (FER) plays a vital role in the field of human-computer interaction. To achieve automatic FER, various approaches based on deep learning (DL) have been presented. However, most of them lack for the extraction of discriminative expression semantic information and suffer from the problem of annotation ambiguity.

HAMPLE: deciphering TF-DNA binding mechanism in different cellular environments by characterizing higher-order nucleotide dependency.

Motivation: Transcription factor (TF) binds to conservative DNA binding sites in different cellular environments and development stages by physical interaction with interdependent nucleotides. However, systematic computational characterization of the relationship between higher-order nucleotide dependency and TF-DNA binding mechanism in diverse cell types remains challenging.

Results: Here, we propose a novel multi-task learning framework HAMPLE to simultaneously predict TF binding sites (TFBS) in distinct cell types by characterizing higher-order nucleotide dependencies.

CSF-GTNet: A Novel Multi-Dimensional Feature Fusion Network Based on Convnext-GeLU- BiLSTM for EEG-Signals-Enabled Fatigue Driving Detection.

Electroencephalography (EEG) signal has been recognized as an effective fatigue detection method, which can intuitively reflect the drivers' mental state. However, the research on multi-dimensional features in existing work could be much better. The instability and complexity of EEG signals will increase the difficulty of extracting data features.

DeSeg: auto detector-based segmentation for brain metastases.

Delineation of brain metastases (BMs) is a paramount step in stereotactic radiosurgery treatment. Clinical practice has specific expectation on BM auto-delineation that the method is supposed to avoid missing of small lesions and yield accurate contours for large lesions. In this study, we propose a novel coarse-to-fine framework, named detector-based segmentation (DeSeg), to incorporate object-level detection into pixel-wise segmentation so as to meet the clinical demand.

MNAS: Multi-Scale and Multi-Level Memory-Efficient Neural Architecture Search for Low-Dose CT Denoising.

Lowering the radiation dose in computed tomography (CT) can greatly reduce the potential risk to public health. However, the reconstructed images from dose-reduced CT or low-dose CT (LDCT) suffer from severe noise which compromises the subsequent diagnosis and analysis. Recently, convolutional neural networks have achieved promising results in removing noise from LDCT images.

Towards a better understanding of TF-DNA binding prediction from genomic features.

Transcription factors (TFs) can regulate gene expression by recognizing specific cis-regulatory elements in DNA sequences. TF-DNA binding prediction has become a fundamental step in comprehending the underlying cis-regulation mechanism. Since a particular genome region is bound depending on multiple features, such as the arrangement of nucleotides, DNA shape, and an epigenetic mechanism, many researchers attempt to develop computational methods to predict TF binding sites (TFBSs) based on various genomic features.

An Efficient Semi-Supervised Framework with Multi-Task and Curriculum Learning for Medical Image Segmentation.

A practical problem in supervised deep learning for medical image segmentation is the lack of labeled data which is expensive and time-consuming to acquire. In contrast, there is a considerable amount of unlabeled data available in the clinic. To make better use of the unlabeled data and improve the generalization on limited labeled data, in this paper, a novel semi-supervised segmentation method via multi-task curriculum learning is presented.

Semi-supervised medical image segmentation via a tripled-uncertainty guided mean teacher model with contrastive learning.

Due to the difficulty in accessing a large amount of labeled data, semi-supervised learning is becoming an attractive solution in medical image segmentation. To make use of unlabeled data, current popular semi-supervised methods (e.g.

Training low dose CT denoising network without high quality reference data.

Currently, the field of low-dose CT (LDCT) denoising is dominated by supervised learning based methods, which need perfectly registered pairs of LDCT and its corresponding clean reference image (normal-dose CT). However, training without clean labels is more practically feasible and significant, since it is clinically impossible to acquire a large amount of these paired samples. In this paper, a self-supervised denoising method is proposed for LDCT imaging.

A survey on the algorithm and development of multiple sequence alignment.

Multiple sequence alignment (MSA) is an essential cornerstone in bioinformatics, which can reveal the potential information in biological sequences, such as function, evolution and structure. MSA is widely used in many bioinformatics scenarios, such as phylogenetic analysis, protein analysis and genomic analysis. However, MSA faces new challenges with the gradual increase in sequence scale and the increasing demand for alignment accuracy.

ISSMF: Integrated semantic and spatial information of multi-level features for automatic segmentation in prenatal ultrasound images.

As an effective way of routine prenatal diagnosis, ultrasound (US) imaging has been widely used recently. Biometrics obtained from the fetal segmentation shed light on fetal health monitoring. However, the segmentation in US images has strict requirements for sonographers on accuracy, making this task quite time-consuming and tedious.

FONT-SIR: Fourth-Order Nonlocal Tensor Decomposition Model for Spectral CT Image Reconstruction.

Spectral computed tomography (CT) reconstructs images from different spectral data through photon counting detectors (PCDs). However, due to the limited number of photons and the counting rate in the corresponding spectral segment, the reconstructed spectral images are usually affected by severe noise. In this paper, we propose a fourth-order nonlocal tensor decomposition model for spectral CT image reconstruction (FONT-SIR).

Diffusion MRI super-resolution reconstruction via sub-pixel convolution generative adversarial network.

To solve the problem of long sampling time for diffusion magnetic resonance imaging (dMRI), in this study we propose a dMRI super-resolution reconstruction network. This method not only uses a three-dimensional (3D) convolution kernel to reconstruct the dMRI data in the space and angle domains, but also introduces an adversarial learning and attention mechanism to solve the problem of the traditional loss function not fully quantifying the gap between high-dimensional data and not paying more attention to important feature maps. Experimental results from the comparison of peak signal-to-noise ratio, structural similarity, and orientation distribution function visualization show that these methods bring better results.

Multi-constraint generative adversarial network for dose prediction in radiotherapy.

Radiation therapy (RT) is regarded as the primary treatment for cancer in the clinic, aiming to deliver an accurate dose to the planning target volume (PTV) while protecting the surrounding organs at risk (OARs). To improve the effectiveness of the treatment planning, deep learning methods are widely adopted to predict dose distribution maps for clinical treatment planning. In this paper, we present a novel multi-constraint dose prediction model based on generative adversarial network, named Mc-GAN, to automatically predict the dose distribution map from the computer tomography (CT) images and the masks of PTV and OARs.