TAC-UNet: transformer-assisted convolutional neural network for medical image segmentation.

Background: Medical image segmentation is crucial for improving healthcare outcomes. Convolutional neural networks (CNNs) have been widely applied in medical image analysis; however, their inherent inductive biases limit their ability to capture global contextual information. Vision transformer (ViT) architectures address this limitation by leveraging attention mechanisms to model global relationships; however, they typically require large-scale datasets for effective training, which is challenging in the field of medical imaging due to limited data availability.

Ancient Yi Script Handwriting Sample Repository.

The ancient Yi script has been used for over 8000 years, which can be ranked with Oracle,Sumerian,Egyptian,Mayan and Harappan,and is one of the six ancient scripts in the world. In this article, we collected 2922 handwritten single word samples of commonly used ancient Yi characters. Each character was written by 310 people respectively, with a total of 427,939 valid characters.

Microscale Normal Compression Behaviors of Clay Aggregates: A Coarse-Grained Molecular Dynamics Study.

Given the limitations of micromechanical experiments and molecular dynamics simulations, the normal compression process of clay aggregates was simulated under different vertical pressures (), numbers of particles, loading methods, and environments by a Gay-Berne potential model. On the basis of the variations of particle orientation and the distribution of stacks, the evolution of deformation and stresses was elucidated. The results showed that the effects of the pressure level and loading environment on the deformation were significant.

The clinical and imaging data fusion model for single-period cerebral CTA collateral circulation assessment.

Background: The Chinese population ranks among the highest globally in terms of stroke prevalence. In the clinical diagnostic process, radiologists utilize computed tomography angiography (CTA) images for diagnosis, enabling a precise assessment of collateral circulation in the brains of stroke patients. Recent studies frequently combine imaging and machine learning methods to develop computer-aided diagnostic algorithms.

Quantitative imaging for predicting hematoma expansion in intracerebral hemorrhage: A multimodel comparison.

Background: Several studies report that radiomics provides additional information for predicting hematoma expansion in intracerebral hemorrhage (ICH). However, the comparison of diagnostic performance of radiomics for predicting revised hematoma expansion (RHE) remains unclear.

Methods: The cohort comprised 312 consecutive patients with ICH.

ETUNet:Exploring efficient transformer enhanced UNet for 3D brain tumor segmentation.

Medical image segmentation is a crucial topic in medical image processing. Accurately segmenting brain tumor regions from multimodal MRI scans is essential for clinical diagnosis and survival prediction. However, similar intensity distributions, variable tumor shapes, and fuzzy boundaries pose severe challenges for brain tumor segmentation.

Optimizing DUS testing for using feature selection based on a genetic algorithm.

is a famous traditional flower in China with high ornamental value. It has numerous varieties, yet its classification is highly disorganized. The distinctness, uniformity, and stability (DUS) test enables the classification and nomenclature of various species; thus, it can be used to classify the varieties.

A fusion model integrating magnetic resonance imaging radiomics and deep learning features for predicting alpha-thalassemia X-linked intellectual disability mutation status in isocitrate dehydrogenase-mutant high-grade astrocytoma: a multicenter study.

Background: The mutational status of alpha-thalassemia X-linked intellectual disability () is an important indicator for the treatment and prognosis of high-grade gliomas, but reliable testing currently requires invasive procedures. The objective of this study was to develop a clinical trait-imaging fusion model that combines preoperative magnetic resonance imaging (MRI) radiomics and deep learning (DL) features with clinical variables to predict status in isocitrate dehydrogenase ()-mutant high-grade astrocytoma.

Methods: A total of 234 patients with -mutant high-grade astrocytoma (120 mutant type, 114 wild type) from 3 centers were retrospectively analyzed.

LVONet: automatic classification model for large vessel occlusion based on the difference information between left and right hemispheres.

Stroke is a highly lethal condition, with intracranial vessel occlusion being one of its primary causes. Intracranial vessel occlusion can typically be categorized into four types, each requiring different intervention measures. Therefore, the automatic and accurate classification of intracranial vessel occlusions holds significant clinical importance for assessing vessel occlusion conditions.

Feasibility and effectiveness of automatic deep learning network and radiomics models for differentiating tumor stroma ratio in pancreatic ductal adenocarcinoma.

Objective: This study aims to compare the feasibility and effectiveness of automatic deep learning network and radiomics models in differentiating low tumor stroma ratio (TSR) from high TSR in pancreatic ductal adenocarcinoma (PDAC).

Methods: A retrospective analysis was conducted on a total of 207 PDAC patients from three centers (training cohort: n = 160; test cohort: n = 47). TSR was assessed on hematoxylin and eosin-stained specimens by experienced pathologists and divided as low TSR and high TSR.

Radiomics models based on cortical damages for identification of multiple sclerosis with cognitive impairment.

Background: Cognitive impairment (CI) is a common symptom in multiple sclerosis (MS) patients. Cortical damages can be closely associated with cognitive network dysfunction and clinically significant CI in MS. So, in this study, We aimed to develop a radiomics model to efficiently identify the MS patients with CI based on clinical data and cortical damages.

Preoperative Discrimination of CDKN2A/B Homozygous Deletion Status in Isocitrate Dehydrogenase-Mutant Astrocytoma: A Deep Learning-Based Radiomics Model Using MRI.

Background: Cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) homozygous deletion has been verified as an independent and critical biomarker of negative prognosis and short survival in isocitrate dehydrogenase (IDH)-mutant astrocytoma. Therefore, noninvasive and accurate discrimination of CDKN2A/B homozygous deletion status is essential for the clinical management of IDH-mutant astrocytoma patients.

Purpose: To develop a noninvasive, robust preoperative model based on MR image features for discriminating CDKN2A/B homozygous deletion status of IDH-mutant astrocytoma.

SFF-Siam: A New Oracle Bone Rejoining Method Based on Siamese Network.

The rejoining of oracle bone rubbings is a fundamental topic in oracle bone inscriptions (OBIs) research. However, the traditional oracle bone (OB) rejoining methods are not only time-consuming and laborious but difficult to apply to large-scale OB rejoining. We proposed a simple OB rejoining model (SFF-Siam) to handle this challenge.

Automatic Evaluating of Multi-Phase Cranial CTA Collateral Circulation Based on Feature Fusion Attention Network Model.

Stroke is one of the main causes of disability and death, and it can be divided into hemorrhagic stroke and ischemic stroke. Ischemic stroke is more common, and about 8 out of 10 stroke patients suffer from ischemic stroke. In clinical practice, doctors diagnose stroke by using computed tomography angiography (CTA) image to accurately evaluate the collateral circulation in stroke patients.

One shot ancient character recognition with siamese similarity network.

Ancient character recognition is not only important for the study and understanding of ancient history but also has a profound impact on the inheritance and development of national culture. In order to reduce the study of difficult professional knowledge of ancient characters, and meanwhile overcome the lack of data, class imbalance, diversification of glyphs, and open set recognition problems in ancient characters, we propose a Siamese similarity network based on a similarity learning method to directly learn input similarity and then apply the trained model to establish one shot classification task for recognition. Multi-scale fusion backbone structure and embedded structure are proposed in the network to improve the model's ability to extract features.

Multicenter clinical radiomics-integrated model based on [F]FDG PET and multi-modal MRI predict ATRX mutation status in IDH-mutant lower-grade gliomas.

Objectives: To develop a clinical radiomics-integrated model based on F-fluorodeoxyglucose positron emission tomography ([F]FDG PET) and multi-modal MRI for predicting alpha thalassemia/mental retardation X-linked (ATRX) mutation status of IDH-mutant lower-grade gliomas (LGGs).

Methods: One hundred and two patients (47 ATRX mutant-type, 55 ATRX wild-type) diagnosed with IDH-mutant LGGs (CNS WHO grades 1 and 2) were retrospectively enrolled. A total of 5540 radiomics features were extracted from structural MR (sMR) images (contrast-enhanced T1-weighted imaging, CE-T1WI; T2-weighted imaging, and T2WI), functional MR (fMR) images (apparent diffusion coefficient, ADC; cerebral blood volume, CBV), and metabolic PET images ([F]FDG PET).

Merging Multiphase CTA Images and Training Them Simultaneously with a Deep Learning Algorithm Could Improve the Efficacy of AI Models for Lateral Circulation Assessment in Ischemic Stroke.

We aimed to build a deep learning-based, objective, fast, and accurate collateral circulation assessment model. We included 92 patients who had suffered acute ischemic stroke (AIS) with large vessel occlusion in the anterior circulation in this study, following their admission to our hospital from June 2020 to August 2021. We analyzed their baseline whole-brain four-dimensional computed tomography angiography (4D-CTA)/CT perfusion.

Clinical Variables, Deep Learning and Radiomics Features Help Predict the Prognosis of Adult Anti-N-methyl-D-aspartate Receptor Encephalitis Early: A Two-Center Study in Southwest China.

Objective: To develop a fusion model combining clinical variables, deep learning (DL), and radiomics features to predict the functional outcomes early in patients with adult anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis in Southwest China.

Methods: From January 2012, a two-center study of anti-NMDAR encephalitis was initiated to collect clinical and MRI data from acute patients in Southwest China. Two experienced neurologists independently assessed the patients' prognosis at 24 moths based on the modified Rankin Scale (mRS) (good outcome defined as mRS 0-2; bad outcome defined as mRS 3-6).

Differentiating solitary brain metastases from glioblastoma by radiomics features derived from MRI and 18F-FDG-PET and the combined application of multiple models.

This study aimed to explore the ability of radiomics derived from both MRI and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) images to differentiate glioblastoma (GBM) from solitary brain metastases (SBM) and to investigate the combined application of multiple models. The imaging data of 100 patients with brain tumours (50 GBMs and 50 SBMs) were retrospectively analysed. Three model sets were built on MRI, 18F-FDG-PET, and MRI combined with 18F-FDG-PET using five feature selection methods and five classification algorithms.

An Integrated Radiomics Model Incorporating Diffusion-Weighted Imaging and F-FDG PET Imaging Improves the Performance of Differentiating Glioblastoma From Solitary Brain Metastases.

Background: The effectiveness of conventional MRI (cMRI)-based radiomics in differentiating glioblastoma (GBM) from solitary brain metastases (SBM) is not satisfactory enough. Therefore, we aimed to develop an integrated radiomics model to improve the performance of differentiating GBM from SBM.

Methods: One hundred patients with solitary brain tumors (50 with GBM, 50 with SBM) were retrospectively enrolled and randomly assigned to the training set ( = 80) or validation set ( = 20).

A Classification Method of Oracle Materials Based on Local Convolutional Neural Network Framework.

The classification of materials of oracle bone is one of the most basic aspects for oracle bone morphology. However, the classification method depending on experts' experience requires long-term learning and accumulation for professional knowledge. This article presents a multiregional convolutional neural network to classify the rubbings of oracle bones.