Superfluorescence and nonlinear optical response of CHNHPbBr-based mesostructures: applications to amplified spontaneous emission and ultrafast lasers.

Metal halide perovskites have unique luminescent properties that make them an attractive alternative for high quality light-emitting devices. However, the poor stability of perovskites with many defects and the long cycle time for the preparation of perovskite nanocomposites have hindered their production and application. Here, we prepared the perovskite mesostructures by embedding MAPbBr nanocrystals in the mesopores on the surface of silica nanospheres and mixing the nanospheres with silver nanowires and poly(methyl methacrylate) (PMMA), and further explored their optical properties.

Nonlinear optical properties of BiSeTe for harmonic mode-locked and broadband Q-switched pulses generation.

The nonlinear optical properties of bismuth selenium telluride (BiSeTe), a few-layer two-dimensional topological insulator material, have been investigated in this work. An erbium-doped fiber laser (EDFL) based on BiSeTe (BST) nanosheets as saturable absorber (SA) was demonstrated. The BST SA with saturation intensity and modulation depth of 4.

A difficulty-aware and task-augmentation method based on meta-learning model for few-shot diabetic retinopathy classification.

Background: Accurate classification techniques are essential for the early diagnosis and treatment of patients with diabetic retinopathy (DR). However, the limited amount of annotated DR data poses a challenge for existing deep-learning models. This article proposes a difficulty-aware and task-augmentation method based on meta-learning (DaTa-ML) model for few-shot DR classification with fundus images.

Dual contrast attention-guided multi-frequency fusion for multi-contrast MRI super-resolution.

. Multi-contrast magnetic resonance (MR) imaging super-resolution (SR) reconstruction is an effective solution for acquiring high-resolution MR images. It utilizes anatomical information from auxiliary contrast images to improve the quality of the target contrast images.

MARS-GAN: Multilevel-Feature-Learning Attention-Aware Based Generative Adversarial Network for Removing Surgical Smoke.

Surgical smoke caused poor visibility during laparoscopic surgery, the smoke removal is important to improve the safety and efficiency of the surgery. We propose the Multilevel-feature-learning Attention-aware based Generative Adversarial Network for Removing Surgical Smoke (MARS-GAN) in this work. MARS-GAN incorporates multilevel smoke feature learning, smoke attention learning, and multi-task learning together.

Image-based scatter correction for cone-beam CT using flip swin transformer U-shape network.

Background: Cone beam computed tomography (CBCT) plays an increasingly important role in image-guided radiation therapy. However, the image quality of CBCT is severely degraded by excessive scatter contamination, especially in the abdominal region, hindering its further applications in radiation therapy.

Purpose: To restore low-quality CBCT images contaminated by scatter signals, a scatter correction algorithm combining the advantages of convolutional neural networks (CNN) and Swin Transformer is proposed.

Axial Attention Convolutional Neural Network for Brain Tumor Segmentation with Multi-Modality MRI Scans.

Accurately identifying tumors from MRI scans is of the utmost importance for clinical diagnostics and when making plans regarding brain tumor treatment. However, manual segmentation is a challenging and time-consuming process in practice and exhibits a high degree of variability between doctors. Therefore, an axial attention brain tumor segmentation network was established in this paper, automatically segmenting tumor subregions from multi-modality MRIs.

The Impact of Different Uses of the Internet on Farmers' Adoption of Soil Testing and Formulated Fertilization Technology in Rural China.

Soil testing and formulated fertilization technology can effectively solve the problem of the excessive and inefficient use of chemical fertilizers. Previous studies have found that the use of the Internet can increase the adoption of soil testing and formulated fertilization technology among farmers. However, they do not distinguish between the effects of the different uses of the Internet (with or without productive use) on the adoption of soil testing and formulated fertilization technology.

CT synthesis from MR in the pelvic area using Residual Transformer Conditional GAN.

Magnetic resonance (MR) image-guided radiation therapy is a hot topic in current radiation therapy research, which relies on MR to generate synthetic computed tomography (SCT) images for radiation therapy. Convolution-based generative adversarial networks (GAN) have achieved promising results in synthesizing CT from MR since the introduction of deep learning techniques. However, due to the local limitations of pure convolutional neural networks (CNN) structure and the local mismatch between paired MR and CT images, particularly in pelvic soft tissue, the performance of GAN in synthesizing CT from MR requires further improvement.

Temperature guided network for 3D joint segmentation of the pancreas and tumors.

Accurate and automatic segmentation of pancreatic tumors and organs from medical images is important for clinical diagnoses and making treatment plans for patients with pancreatic cancer. Although deep learning methods have been widely adopted for this task, the segmentation accuracy, especially for pancreatic tumors, still needs to be further improved because (1) phenotypic differences, such as volumes, tend to make the models focus on pancreatic learning, resulting in insufficient tumor feature selection; (2) deep learning models may fall into local optima, leading to unsatisfactory segmentation results for tumors and pancreas. To alleviate the above issues, in this paper, we propose a 3D fully convolutional neural network with three temperature guided modules, namely, balance temperature loss, rigid temperature optimizer and soft temperature indictor, to realize joint segmentation of the pancreas and tumors.

Third-order harmonic mode-locked and Q-switched Er-doped fiber laser based on a CrGeTe saturable absorber.

This paper reports the generation of fundamental solitons and third-order solitons in an erbium-doped fiber laser (EDFL) by a -polyvinyl alcohol (CGT-PVA) saturable absorber (SA). Stable fundamental solitons at 1559.09 nm at a repetition frequency of 5.

Semi-supervised classification of fundus images combined with CNN and GCN.

Purpose: Diabetic retinopathy (DR) is one of the most serious complications of diabetes, which is a kind of fundus lesion with specific changes. Early diagnosis of DR can effectively reduce the visual damage caused by DR. Due to the variety and different morphology of DR lesions, automatic classification of fundus images in mass screening can greatly save clinicians' diagnosis time.

Radiomics for Predicting Response of Neoadjuvant Chemotherapy in Nasopharyngeal Carcinoma: A Systematic Review and Meta-Analysis.

Purpose: This study examined the methodological quality of radiomics to predict the effectiveness of neoadjuvant chemotherapy in nasopharyngeal carcinoma (NPC). We performed a meta-analysis of radiomics studies evaluating the bias risk and treatment response estimation.

Methods: Our study was conducted through a literature review as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

Common feature learning for brain tumor MRI synthesis by context-aware generative adversarial network.

Multi-modal structural Magnetic Resonance Image (MRI) provides complementary information and has been used widely for diagnosis and treatment planning of gliomas. While machine learning is popularly adopted to process and analyze MRI images, most existing tools are based on complete sets of multi-modality images that are costly and sometimes impossible to acquire in real clinical scenarios. In this work, we address the challenge of multi-modality glioma MRI synthesis often with incomplete MRI modalities.

Multimodality MRI-based radiomics approach to predict the posttreatment response of lung cancer brain metastases to gamma knife radiosurgery.

Objectives: To develop and validate a multimodality MRI-based radiomics approach to predicting the posttreatment response of lung cancer brain metastases (LCBM) to gamma knife radiosurgery (GKRS).

Methods: We retrospectively analyzed 213 lesions from 137 patients with LCBM who received GKRS between January 2017 and November 2020. The data were divided into a primary cohort (102 patients with 173 lesions) and an independent validation cohort (35 patients with 40 lesions) according to the time of treatment.

Red Blood Cell Classification Based on Attention Residual Feature Pyramid Network.

Clinically, red blood cell abnormalities are closely related to tumor diseases, red blood cell diseases, internal medicine, and other diseases. Red blood cell classification is the key to detecting red blood cell abnormalities. Traditional red blood cell classification is done manually by doctors, which requires a lot of manpower produces subjective results.

Differentiation of Brain Abscess From Cystic Glioma Using Conventional MRI Based on Deep Transfer Learning Features and Hand-Crafted Radiomics Features.

To develop and validate the model for distinguishing brain abscess from cystic glioma by combining deep transfer learning (DTL) features and hand-crafted radiomics (HCR) features in conventional T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI). This single-center retrospective analysis involved 188 patients with pathologically proven brain abscess (102) or cystic glioma (86). One thousand DTL and 105 HCR features were extracted from the T1WI and T2WI of the patients.

Generative adversarial networks in medical image segmentation: A review.

Purpose: Since Generative Adversarial Network (GAN) was introduced into the field of deep learning in 2014, it has received extensive attention from academia and industry, and a lot of high-quality papers have been published. GAN effectively improves the accuracy of medical image segmentation because of its good generating ability and capability to capture data distribution. This paper introduces the origin, working principle, and extended variant of GAN, and it reviews the latest development of GAN-based medical image segmentation methods.

CT-Based Hand-crafted Radiomic Signatures Can Predict PD-L1 Expression Levels in Non-small Cell Lung Cancer: a Two-Center Study.

Here, we used pre-treatment CT images to develop and evaluate a radiomic signature that can predict the expression of programmed death ligand 1 (PD-L1) in non-small cell lung cancer (NSCLC). We then verified its predictive performance by cross-referencing its results with clinical characteristics. This two-center retrospective analysis included 125 patients with histologically confirmed NSCLC.

United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast MRI.

Simultaneous segmentation and detection of liver tumors (hemangioma and hepatocellular carcinoma (HCC)) by using multi-modality non-contrast magnetic resonance imaging (NCMRI) are crucial for the clinical diagnosis. However, it is still a challenging task due to: (1) the HCC information on NCMRI is insufficient makes extraction of liver tumors feature difficult; (2) diverse imaging characteristics in multi-modality NCMRI causes feature fusion and selection difficult; (3) no specific information between hemangioma and HCC on NCMRI cause liver tumors detection difficult. In this study, we propose a united adversarial learning framework (UAL) for simultaneous liver tumors segmentation and detection using multi-modality NCMRI.

Towards Dosimetry for Prostate Radiotherapy with a Transperineal Ultrasound Array: A Simulation Study.

X-ray-induced acoustic computed tomography (XACT) is a promising imaging modality to monitor the position of the radiation beam and the deposited dose during external beam radiotherapy delivery. The purpose of this study was to investigate the feasibility of using a transperineal ultrasound transducer array for XACT imaging to guide the prostate radiotherapy. A customized two-dimensional (2D) matrix ultrasound transducer array with 10000 (100×100 elements) ultrasonic sensors with a central frequency of 1 MHz was designed on a 5 cm×5 cm plane to optimize three-dimensional (3D) volumetric imaging.