BSDA: Bayesian Random Semantic Data Augmentation for Medical Image Classification.

Data augmentation is a crucial regularization technique for deep neural networks, particularly in medical imaging tasks with limited data. Deep learning models are highly effective at linearizing features, enabling the alteration of feature semantics through the shifting of latent space representations-an approach known as semantic data augmentation (SDA). The paradigm of SDA involves shifting features in a specified direction.

Deep learning radiomics based on contrast enhanced MRI for preoperatively predicting early recurrence in hepatocellular carcinoma after curative resection.

Purpose: To explore the role of deep learning (DL) and radiomics-based integrated approach based on contrast enhanced magnetic resonance imaging (CEMRI) for predicting early recurrence (ER) in hepatocellular carcinoma (HCC) patients after curative resection.

Methods: Total 165 HCC patients (ER, = 96 vs. non-early recurrence (NER), = 69) were retrospectively collected and divided into a training cohort ( = 132) and a validation cohort ( = 33).

Quantum Dynamical Interpretation of the Mean Strategy.

The method of quantum dynamics is employed to investigate the mean strategy in the swarm intelligence algorithm. The physical significance of the population mean point is explained as the location where the optimal solution with the highest likelihood can be found once a quantum system has reached a ground state. Through the use of the double well function and the CEC2013 test suite, controlled experiments are conducted to perform a comprehensive performance analysis of the mean strategy.

Medical image analysis using improved SAM-Med2D: segmentation and classification perspectives.

Recently emerged SAM-Med2D represents a state-of-the-art advancement in medical image segmentation. Through fine-tuning the Large Visual Model, Segment Anything Model (SAM), on extensive medical datasets, it has achieved impressive results in cross-modal medical image segmentation. However, its reliance on interactive prompts may restrict its applicability under specific conditions.

Deep learning based clinical target volumes contouring for prostate cancer: Easy and efficient application.

Background: Radiotherapy has been crucial in prostate cancer treatment. However, manual segmentation is labor intensive and highly variable among radiation oncologists. In this study, a deep learning based automated contouring model is constructed for clinical target volumes (CTVs) of intact and postoperative prostate cancer.

Self-Powered FTO/CdSe/BiSe Photodetector with Bipolar Photoresponse Characteristics.

Self-powered photodetectors with bipolar photoresponse characteristics are expected to play a critical role in the field of secure optical communication, artificial neuromorphic systems, and intelligent color sensors. In this work, asymmetric heterojunction devices exhibiting wavelength-dependent bipolar photoresponse with a structure of Glass/FTO/CdSe/BiSe/Au were fabricated. Under a short wavelength light irradiation, the top CdSe absorber generates a high carrier concentration; the excited carriers are quickly separated by the built-in electric field induced by the FTO/CdSe diode, resulting in a negative photocurrent.

An epilepsy detection method based on multi-dimensional feature extraction and dual-branch hypergraph convolutional network.

Epilepsy is a disease caused by abnormal neural discharge, which severely harms the health of patients. Its pathogenesis is complex and variable with various forms of seizures, leading to significant differences in epilepsy manifestations among different patients. The changes of brain network are strongly correlated with related pathologies.

Causal association and mediating effect of blood biochemical metabolic traits and brain image-derived endophenotypes on Alzheimer's disease.

Background: Recent genetic evidence supports that circulating biochemical and metabolic traits (BMTs) play a causal role in Alzheimer's disease (AD), which might be mediated by changes in brain structure. Here, we leveraged publicly available genome-wide association study data to investigate the intrinsic causal relationship between blood BMTs, brain image-derived phenotypes (IDPs) and AD.

Methods: Utilizing the genetic variants associated with 760 blood BMTs and 172 brain IDPs as the exposure and the latest AD summary statistics as the outcome, we analyzed the causal relationship between blood BMTs and brain IDPs and AD by using a two-sample Mendelian randomization (MR) method.

An immune-related gene prognostic prediction risk model for neoadjuvant chemoradiotherapy in rectal cancer using artificial intelligence.

Background: This study aimed to establish and validate a prognostic model based on immune-related genes (IRGPM) for predicting disease-free survival (DFS) in patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy, and to elucidate the immune profiles associated with different prognostic outcomes.

Methods: Transcriptomic and clinical data were sourced from the Gene Expression Omnibus (GEO) database and the West China Hospital database. We focused on genes from the RNA immune-oncology panel.

Epileptic focus localization using transfer learning on multi-modal EEG.

The standard treatments for epilepsy are drug therapy and surgical resection. However, around 1/3 of patients with intractable epilepsy are drug-resistant, requiring surgical resection of the epileptic focus. To address the issue of drug-resistant epileptic focus localization, we have proposed a transfer learning method on multi-modal EEG (iEEG and sEEG).

[Non-local attention and multi-task learning based lung segmentation in chest X-ray].

Precise segmentation of lung field is a crucial step in chest radiographic computer-aided diagnosis system. With the development of deep learning, fully convolutional network based models for lung field segmentation have achieved great effect but are poor at accurate identification of the boundary and preserving lung field consistency. To solve this problem, this paper proposed a lung segmentation algorithm based on non-local attention and multi-task learning.

Feature separation and adversarial training for the patient-independent detection of epileptic seizures.

An epileptic seizure is the external manifestation of abnormal neuronal discharges, which seriously affecting physical health. The pathogenesis of epilepsy is complex, and the types of epileptic seizures are diverse, resulting in significant variation in epileptic seizure data between subjects. If we feed epilepsy data from multiple patients directly into the model for training, it will lead to underfitting of the model.

Predicting Rectal Cancer Response to Total Neoadjuvant Treatment Using an Artificial Intelligence Model Based on Magnetic Resonance Imaging and Clinical Data.

Purpose: To develop a model for predicting response to total neoadjuvant treatment (TNT) for patients with locally advanced rectal cancer (LARC) based on baseline magnetic resonance imaging (MRI) and clinical data using artificial intelligence methods.

Methods: Baseline MRI and clinical data were curated from patients with LARC and analyzed using logistic regression (LR) and deep learning (DL) methods to predict TNT response retrospectively. We defined two groups of response to TNT as pathological complete response (pCR) versus non-pCR (Group 1), and high sensitivity [tumor regression grade (TRG) 0 and TRG 1] versus moderate sensitivity (TRG 2 or patients with TRG 3 and a reduction in tumor volume of at least 20% compared to baseline) versus low sensitivity (TRG 3 and a reduction in tumor volume <20% compared to baseline) (Group 2).

Patient-specific approach using data fusion and adversarial training for epileptic seizure prediction.

Epilepsy is the second common neurological disorder after headache, accurate and reliable prediction of seizures is of great clinical value. Most epileptic seizure prediction methods consider only the EEG signal or extract and classify the features of EEG and ECG signals separately, the improvement of prediction performance from multimodal data is not fully considered. In addition, epilepsy data are time-varying, with differences between each episode in a patient, making it difficult for traditional curve-fitting models to achieve high accuracy and reliability.

Identifying novel genes for amyotrophic lateral sclerosis by integrating human brain proteomes with genome-wide association data.

Background: Genome-Wide Association Studies (GWAS) have identified numerous risk genes for Amyotrophic Lateral Sclerosis (ALS); however, the mechanisms by which these loci confer ALS risk are uncertain. This study aims to identify novel causal proteins in the brains of patients with ALS using an integrative analytical pipeline.

Methods: Using the datasets of Protein Quantitative Trait Loci (pQTL) (N = 376, N = 152), expression QTL (eQTL) (N = 452), and the largest ALS GWAS (N27,205, N = 110,881), we performed a systematic analytical pipeline including Proteome-Wide Association Study (PWAS), Mendelian Randomization (MR), Bayesian colocalization, and Transcriptome-Wide Association Study (TWAS) to identify novel causal proteins for ALS in the brain.

[A meta-learning based method for segmentation of few-shot magnetic resonance images].

When applying deep learning algorithms to magnetic resonance (MR) image segmentation, a large number of annotated images are required as data support. However, the specificity of MR images makes it difficult and costly to acquire large amounts of annotated image data. To reduce the dependence of MR image segmentation on a large amount of annotated data, this paper proposes a meta-learning U-shaped network (Meta-UNet) for few-shot MR image segmentation.

Segmentation of rectal tumor from multi-parametric MRI images using an attention-based fusion network.

Accurate segmentation of rectal tumors is the most crucial task in determining the stage of rectal cancer and developing suitable therapies. However, complex image backgrounds, irregular edge, and poor contrast hinder the related research. This study presents an attention-based multi-modal fusion module to effectively integrate complementary information from different MRI images and suppress redundancy.

Identifying novel proteins underlying loneliness by integrating GWAS summary data with human brain proteomes.

Enduring loneliness is associated with mental disorders and physical diseases. Although genome-wide association studies (GWAS) have identified risk loci associated with loneliness, how these loci confer the risk remains largely unknown. In the current study, we aimed to investigate key proteins underlying loneliness in the brain by integrating human brain proteomes and transcriptomes with loneliness GWAS to perform a discovery proteome-wide association study (PWAS), followed by a confirmatory PWAS, transcriptome-wide association analysis (TWAS), Mendelian randomization (MR), Steigering filtering analysis and Bayesian colocalization analysis.

Feasibility of intelligent drug control in the maintenance phase of general anesthesia based on convolutional neural network.

Background: The growth and aging process of the human population has accelerated the increase in surgical procedures. Yet, the demand for increasing operations can be hardly met since the training of anesthesiologists is usually a long-term process. Closed-loop artificial intelligence (AI) model provides the possibility to solve intelligent decision-making for anesthesia auxiliary control and, as such, has allowed breakthroughs in closed-loop control of clinical practices in intensive care units (ICUs).

High-Aperture-Ratio Dual-View Integral Imaging Display.

Low aperture ratio is a problem in the conventional dual-view integral imaging (DVII) display using a point light source array. A high-aperture-ratio DVII display using a gradient width point light source array is reported in this work. The elemental Images 1 and 2, which are alternatively aligned on a liquid crystal panel, are illuminated by the light rays emitted from an assigned point light source.

Identifying novel proteins underlying schizophrenia via integrating pQTLs of the plasma, CSF, and brain with GWAS summary data.

Background: Schizophrenia (SCZ) is a chronic and severe mental illness with no cure so far. Mendelian randomization (MR) is a genetic method widely used to explore etiologies of complex traits. In the current study, we aimed to identify novel proteins underlying SCZ with a systematic analytical approach.

Non-Local Temporal Difference Network for Temporal Action Detection.

As an important part of video understanding, temporal action detection (TAD) has wide application scenarios. It aims to simultaneously predict the boundary position and class label of every action instance in an untrimmed video. Most of the existing temporal action detection methods adopt a stacked convolutional block strategy to model long temporal structures.

Peripheral level of CD33 and Alzheimer's disease: a bidirectional two-sample Mendelian randomization study.

Increased expression of CD33 in the brain has been suggested to be associated with increased amyloid plaque burden, while the peripheral level of CD33 in Alzheimer's disease (AD) patients and its role in AD remain unclear. The current study aimed to systematically explore the bidirectional relationship between peripheral CD33 and AD. Genome-wide association study (GWAS) datasets of AD (N: 21982; N: 41944), blood CD33 mRNA level, the plasma CD33 protein level, and CD33 expression on immune-cell subtypes were obtained from GWASs conducted in the European population.