Dipolar-glass-like relaxor ferroelectric behaviour in the 0.5BaTiO3-0.5Bi(Mg1/2Ti1/2)O3 electroceramic

In this study, the dielectric and ferroelectric switching behaviour of 0.5BaTiO3-0.5Bi(Mg1/2Ti1/2)O3 (BT-BMT) ceramics are investigated. The BT-BMT ceramic exhibits a typical dipolar-glass-like, dielectric polarisation relaxation. This is attributed to the 15 distinct possible local A4B2 configurations around the O ions and the effect this unavoidable local compositional variability has on the dipole relaxation behaviour of inherent {1-D h111} dipole chains, arising from correlated off-centre displacements of Bi3+ and Ti4+ ions along local {111} directions. On the other hand, switchable polarisation under strong applied electric fields is observed on different length scales accompanied by the appearance of strong polarisation relaxation, as observed via time-delayed piezoresponse hysteresis loop measurements. These experimental results demonstrate that this BT-BMT ceramic is relaxor ferroelectric in nature, although it exhibits dipolar-glass-like dielectric relaxation behaviour.The authors J.W., Y.L., and R.L.W. acknowledge the support of the Australian Research Council (ARC) in the form of Discovery projects. Y.L. also appreciates support from the ARC Future Fellowships program

NAS-ASDet: An Adaptive Design Method for Surface Defect Detection Network using Neural Architecture Search

Deep convolutional neural networks (CNNs) have been widely used in surface defect detection. However, no CNN architecture is suitable for all detection tasks and designing effective task-specific requires considerable effort. The neural architecture search (NAS) technology makes it possible to automatically generate adaptive data-driven networks. Here, we propose a new method called NAS-ASDet to adaptively design network for surface defect detection. First, a refined and industry-appropriate search space that can adaptively adjust the feature distribution is designed, which consists of repeatedly stacked basic novel cells with searchable attention operations. Then, a progressive search strategy with a deep supervision mechanism is used to explore the search space faster and better. This method can design high-performance and lightweight defect detection networks with data scarcity in industrial scenarios. The experimental results on four datasets demonstrate that the proposed method achieves superior performance and a relatively lighter model size compared to other competitive methods, including both manual and NAS-based approaches

MeLo: Low-rank Adaptation is Better than Fine-tuning for Medical Image Diagnosis

The common practice in developing computer-aided diagnosis (CAD) models based on transformer architectures usually involves fine-tuning from ImageNet pre-trained weights. However, with recent advances in large-scale pre-training and the practice of scaling laws, Vision Transformers (ViT) have become much larger and less accessible to medical imaging communities. Additionally, in real-world scenarios, the deployments of multiple CAD models can be troublesome due to problems such as limited storage space and time-consuming model switching. To address these challenges, we propose a new method MeLo (Medical image Low-rank adaptation), which enables the development of a single CAD model for multiple clinical tasks in a lightweight manner. It adopts low-rank adaptation instead of resource-demanding fine-tuning. By fixing the weight of ViT models and only adding small low-rank plug-ins, we achieve competitive results on various diagnosis tasks across different imaging modalities using only a few trainable parameters. Specifically, our proposed method achieves comparable performance to fully fine-tuned ViT models on four distinct medical imaging datasets using about 0.17% trainable parameters. Moreover, MeLo adds only about 0.5MB of storage space and allows for extremely fast model switching in deployment and inference. Our source code and pre-trained weights are available on our website (https://absterzhu.github.io/melo.github.io/).Comment: 5 pages, 3 figure

AdLER: Adversarial Training with Label Error Rectification for One-Shot Medical Image Segmentation

Accurate automatic segmentation of medical images typically requires large datasets with high-quality annotations, making it less applicable in clinical settings due to limited training data. One-shot segmentation based on learned transformations (OSSLT) has shown promise when labeled data is extremely limited, typically including unsupervised deformable registration, data augmentation with learned registration, and segmentation learned from augmented data. However, current one-shot segmentation methods are challenged by limited data diversity during augmentation, and potential label errors caused by imperfect registration. To address these issues, we propose a novel one-shot medical image segmentation method with adversarial training and label error rectification (AdLER), with the aim of improving the diversity of generated data and correcting label errors to enhance segmentation performance. Specifically, we implement a novel dual consistency constraint to ensure anatomy-aligned registration that lessens registration errors. Furthermore, we develop an adversarial training strategy to augment the atlas image, which ensures both generation diversity and segmentation robustness. We also propose to rectify potential label errors in the augmented atlas images by estimating segmentation uncertainty, which can compensate for the imperfect nature of deformable registration and improve segmentation authenticity. Experiments on the CANDI and ABIDE datasets demonstrate that the proposed AdLER outperforms previous state-of-the-art methods by 0.7% (CANDI), 3.6% (ABIDE "seen"), and 4.9% (ABIDE "unseen") in segmentation based on Dice scores, respectively. The source code will be available at https://github.com/hsiangyuzhao/AdLER

Image Synthesis with Disentangled Attributes for Chest X-Ray Nodule Augmentation and Detection

Lung nodule detection in chest X-ray (CXR) images is common to early screening of lung cancers. Deep-learning-based Computer-Assisted Diagnosis (CAD) systems can support radiologists for nodule screening in CXR. However, it requires large-scale and diverse medical data with high-quality annotations to train such robust and accurate CADs. To alleviate the limited availability of such datasets, lung nodule synthesis methods are proposed for the sake of data augmentation. Nevertheless, previous methods lack the ability to generate nodules that are realistic with the size attribute desired by the detector. To address this issue, we introduce a novel lung nodule synthesis framework in this paper, which decomposes nodule attributes into three main aspects including shape, size, and texture, respectively. A GAN-based Shape Generator firstly models nodule shapes by generating diverse shape masks. The following Size Modulation then enables quantitative control on the diameters of the generated nodule shapes in pixel-level granularity. A coarse-to-fine gated convolutional Texture Generator finally synthesizes visually plausible nodule textures conditioned on the modulated shape masks. Moreover, we propose to synthesize nodule CXR images by controlling the disentangled nodule attributes for data augmentation, in order to better compensate for the nodules that are easily missed in the detection task. Our experiments demonstrate the enhanced image quality, diversity, and controllability of the proposed lung nodule synthesis framework. We also validate the effectiveness of our data augmentation on greatly improving nodule detection performance

Single-shot compressed ultrafast photography: a review

Compressed ultrafast photography (CUP) is a burgeoning single-shot computational imaging technique that provides an imaging speed as high as 10 trillion frames per second and a sequence depth of up to a few hundred frames. This technique synergizes compressed sensing and the streak camera technique to capture nonrepeatable ultrafast transient events with a single shot. With recent unprecedented technical developments and extensions of this methodology, it has been widely used in ultrafast optical imaging and metrology, ultrafast electron diffraction and microscopy, and information security protection. We review the basic principles of CUP, its recent advances in data acquisition and image reconstruction, its fusions with other modalities, and its unique applications in multiple research fields

Silencing of c-Ski augments TGF-b1-induced epithelial-mesenchymal transition in cardiomyocyte H9C2 cells

Background: The shRNA lentiviral vector was constructed to silence c-Ski expression in cardiac mus-  cle cells, with the aim of exploring the role of c-Ski in transforming growth factor b1 (TGF-b1)-induced epithelial-mesenchymal transitions (EMT) in H9C2 cells. Methods: Real-time polymerase chain reaction (RT-PCR) and western blot were used to detect c-Ski ex- pression at protein and messenger ribonucleic acid (mRNA) levels in 5 different cell lines. Then, lentiviral vector was constructed to silence or overexpress c-Ski in H9C2 cells. MTT and/or soft agar assay and tran- swell assay were used to detect cell proliferation and migration, respectively. The expression levels of c-Ski under different concentrations of TGF-b1 stimulation were detected by RT-qPCR and immunocytochemi- cal analysis. In the presence or absence of TGF-b1 stimulation, the proteins’ expression levels of a-SMA, FN and E-cadherin, which are closely correlated with the process of EMT, were measured by western blot after c-Ski silencing or overexpression. Meanwhile, the effect of c-Ski on Samd3 phosphorylation with TGF-b1 stimulation was investigated.  Results: There is a high expression of c-Ski at protein and mRNA levels in H9C2 cell line, which first demonstrated the presence of c-Ski expression in H9C2 cells. Overexpression of c-Ski significantly increased H9C2 cell proliferation. The ability of c-Ski gene silencing to suppress cell proliferation was gradually enhanced, and inhibition efficiency was the highest after 6 to 7 d of transfection. Moreover, H9C2 cells with c-Ski knockdown gained significantly aggressive invasive potential when compared with the control group. TGF-b1 stimulation could dose-independently reduce c-Ski expression in H9C2 cells and lead to obvious down-regulated expression of E-cadherin. Interestingly, c-Ski could restore E-cadherin expression while suppressing a-SMA and/or FN expression stimulated by TGF-b1. How- ever, shRNA-induced c-Ski knockdown aggravated only the TGF-b1-induced EMT. Moreover, c-Ski- -shRNA also promoted the phosphorylation of Samd3 induced by TGF-b1.  Conclusions: c-Ski expression in cardiac muscle cells could be down-regulated by TGF-b1. Silencing of c-Ski gene was accompanied by down-regulation of E-cadherin, up-regulation of a-SMA and/or FN and Smad3 phosphorylation induced by TGF-b1, promoting EMT process. Therefore, c-Ski may be closely associated with TGF-b1-induced EMT and play an important role in cardiac fibrosis develop- ment and progression.

Switching spectroscopic measurement of surface potentials on ferroelectric surfaces via an open-loop Kelvin probe force microscopy method

We report a method for switching spectroscopy Kelvin probe force microscopy (SS-KPFM). The method is established as a counterpart to switching spectroscopy piezoresponse force microscopy (SS-PFM) in Kelvin probe force microscopy. SS-KPFM yields quantitative information about the surface charge state during a local bias-induced polarization switching process, complementary to the electromechanical coupling properties probed via SS-PFM. Typical ferroelectric samples of a Pb-based relaxor single crystal and a BiFeO3 thin film were investigated using both methods. We briefly discuss the observed surfacecharging phenomena and their influence on the associated piezoresponse hysteresis loops.Q.L., Y.L., D.W., and R.L.W. acknowledge the support of the Australian Research Council (ARC) in the form of ARC Discovery Grants. Y.L. also acknowledges support from the ARC Future Fellowships Program

Uni-COAL: A Unified Framework for Cross-Modality Synthesis and Super-Resolution of MR Images

Cross-modality synthesis (CMS), super-resolution (SR), and their combination (CMSR) have been extensively studied for magnetic resonance imaging (MRI). Their primary goals are to enhance the imaging quality by synthesizing the desired modality and reducing the slice thickness. Despite the promising synthetic results, these techniques are often tailored to specific tasks, thereby limiting their adaptability to complex clinical scenarios. Therefore, it is crucial to build a unified network that can handle various image synthesis tasks with arbitrary requirements of modality and resolution settings, so that the resources for training and deploying the models can be greatly reduced. However, none of the previous works is capable of performing CMS, SR, and CMSR using a unified network. Moreover, these MRI reconstruction methods often treat alias frequencies improperly, resulting in suboptimal detail restoration. In this paper, we propose a Unified Co-Modulated Alias-free framework (Uni-COAL) to accomplish the aforementioned tasks with a single network. The co-modulation design of the image-conditioned and stochastic attribute representations ensures the consistency between CMS and SR, while simultaneously accommodating arbitrary combinations of input/output modalities and thickness. The generator of Uni-COAL is also designed to be alias-free based on the Shannon-Nyquist signal processing framework, ensuring effective suppression of alias frequencies. Additionally, we leverage the semantic prior of Segment Anything Model (SAM) to guide Uni-COAL, ensuring a more authentic preservation of anatomical structures during synthesis. Experiments on three datasets demonstrate that Uni-COAL outperforms the alternatives in CMS, SR, and CMSR tasks for MR images, which highlights its generalizability to wide-range applications

A flexible and accurate total variation and cascaded denoisers-based image reconstruction algorithm for hyperspectrally compressed ultrafast photography

Hyperspectrally compressed ultrafast photography (HCUP) based on compressed sensing and the time- and spectrum-to-space mappings can simultaneously realize the temporal and spectral imaging of non-repeatable or difficult-to-repeat transient events passively in a single exposure. It possesses an incredibly high frame rate of tens of trillions of frames per second and a sequence depth of several hundred, and plays a revolutionary role in single-shot ultrafast optical imaging. However, due to the ultra-high data compression ratio induced by the extremely large sequence depth as well as the limited fidelities of traditional reconstruction algorithms over the reconstruction process, HCUP suffers from a poor image reconstruction quality and fails to capture fine structures in complex transient scenes. To overcome these restrictions, we propose a flexible image reconstruction algorithm based on the total variation (TV) and cascaded denoisers (CD) for HCUP, named the TV-CD algorithm. It applies the TV denoising model cascaded with several advanced deep learning-based denoising models in the iterative plug-and-play alternating direction method of multipliers framework, which can preserve the image smoothness while utilizing the deep denoising networks to obtain more priori, and thus solving the common sparsity representation problem in local similarity and motion compensation. Both simulation and experimental results show that the proposed TV-CD algorithm can effectively improve the image reconstruction accuracy and quality of HCUP, and further promote the practical applications of HCUP in capturing high-dimensional complex physical, chemical and biological ultrafast optical scenes.Comment: 25 pages, 5 figures and 1 tabl