K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment

The problem of how to assess cross-modality medical image synthesis has been largely unexplored. The most used measures like PSNR and SSIM focus on analyzing the structural features but neglect the crucial lesion location and fundamental k-space speciality of medical images. To overcome this problem, we propose a new metric K-CROSS to spur progress on this challenging problem. Specifically, K-CROSS uses a pre-trained multi-modality segmentation network to predict the lesion location, together with a tumor encoder for representing features, such as texture details and brightness intensities. To further reflect the frequency-specific information from the magnetic resonance imaging principles, both k-space features and vision features are obtained and employed in our comprehensive encoders with a frequency reconstruction penalty. The structure-shared encoders are designed and constrained with a similarity loss to capture the intrinsic common structural information for both modalities. As a consequence, the features learned from lesion regions, k-space, and anatomical structures are all captured, which serve as our quality evaluators. We evaluate the performance by constructing a large-scale cross-modality neuroimaging perceptual similarity (NIRPS) dataset with 6,000 radiologist judgments. Extensive experiments demonstrate that the proposed method outperforms other metrics, especially in comparison with the radiologists on NIRPS

EasyNet: An Easy Network for 3D Industrial Anomaly Detection

3D anomaly detection is an emerging and vital computer vision task in industrial manufacturing (IM). Recently many advanced algorithms have been published, but most of them cannot meet the needs of IM. There are several disadvantages: i) difficult to deploy on production lines since their algorithms heavily rely on large pre-trained models; ii) hugely increase storage overhead due to overuse of memory banks; iii) the inference speed cannot be achieved in real-time. To overcome these issues, we propose an easy and deployment-friendly network (called EasyNet) without using pre-trained models and memory banks: firstly, we design a multi-scale multi-modality feature encoder-decoder to accurately reconstruct the segmentation maps of anomalous regions and encourage the interaction between RGB images and depth images; secondly, we adopt a multi-modality anomaly segmentation network to achieve a precise anomaly map; thirdly, we propose an attention-based information entropy fusion module for feature fusion during inference, making it suitable for real-time deployment. Extensive experiments show that EasyNet achieves an anomaly detection AUROC of 92.6% without using pre-trained models and memory banks. In addition, EasyNet is faster than existing methods, with a high frame rate of 94.55 FPS on a Tesla V100 GPU

Real3D-AD: A Dataset of Point Cloud Anomaly Detection

High-precision point cloud anomaly detection is the gold standard for identifying the defects of advancing machining and precision manufacturing. Despite some methodological advances in this area, the scarcity of datasets and the lack of a systematic benchmark hinder its development. We introduce Real3D-AD, a challenging high-precision point cloud anomaly detection dataset, addressing the limitations in the field. With 1,254 high-resolution 3D items from forty thousand to millions of points for each item, Real3D-AD is the largest dataset for high-precision 3D industrial anomaly detection to date. Real3D-AD surpasses existing 3D anomaly detection datasets available regarding point cloud resolution (0.0010mm-0.0015mm), 360 degree coverage and perfect prototype. Additionally, we present a comprehensive benchmark for Real3D-AD, revealing the absence of baseline methods for high-precision point cloud anomaly detection. To address this, we propose Reg3D-AD, a registration-based 3D anomaly detection method incorporating a novel feature memory bank that preserves local and global representations. Extensive experiments on the Real3D-AD dataset highlight the effectiveness of Reg3D-AD. For reproducibility and accessibility, we provide the Real3D-AD dataset, benchmark source code, and Reg3D-AD on our website:https://github.com/M-3LAB/Real3D-AD

IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing

Image anomaly detection (IAD) is an emerging and vital computer vision task in industrial manufacturing (IM). Recently many advanced algorithms have been published, but their performance deviates greatly. We realize that the lack of actual IM settings most probably hinders the development and usage of these methods in real-world applications. As far as we know, IAD methods are not evaluated systematically. As a result, this makes it difficult for researchers to analyze them because they are designed for different or special cases. To solve this problem, we first propose a uniform IM setting to assess how well these algorithms perform, which includes several aspects, i.e., various levels of supervision (unsupervised vs. semi-supervised), few-shot learning, continual learning, noisy labels, memory usage, and inference speed. Moreover, we skillfully build a comprehensive image anomaly detection benchmark (IM-IAD) that includes 16 algorithms on 7 mainstream datasets with uniform settings. Our extensive experiments (17,017 in total) provide in-depth insights for IAD algorithm redesign or selection under the IM setting. Next, the proposed benchmark IM-IAD gives challenges as well as directions for the future. To foster reproducibility and accessibility, the source code of IM-IAD is uploaded on the website, https://github.com/M-3LAB/IM-IAD

Synthesis and Biological Evaluation of 4β-N-Acetylamino Substituted Podophyllotoxin Derivatives as Novel Anticancer Agents

A series of novel podophyllotoxin derivatives obtained by 4β-N-acetylamino substitution at C-4 position was designed, synthesized, and evaluated for in vitro cytotoxicity against four human cancer cell lines (EC-9706, HeLA, T-24 and H460) and a normal human epidermal cell line (HaCaT). The cytotoxicity test indicated that most of the derivatives displayed potent anticancer activities. In particular, compound 12h showed high activity with IC50 values ranging from 1.2 to 22.8 μM, with much better cytotoxic activity than the control drug etoposide (IC50: 8.4 to 78.2 μM). Compound 12j exhibited a promising cytotoxicity and selectivity profile against T24 and HaCaT cell lines with IC50 values of 2.7 and 49.1 μM, respectively. Compound 12g displayed potent cytotoxicity against HeLA and T24 cells with low activity against HaCaT cells. According to the results of fluorescence-activated cell sorting (FACS) analysis, 12g induced cell cycle arrest in the G2/M phase accompanied by apoptosis in T24 and HeLA cells. Furthermore, the docking studies showed possible interactions between human DNA topoisomerase IIα and 12g. These results suggest that 12g merits further optimization and development as a new podophyllotoxin-derived lead compound

3D Soft-Landing Dynamic Theoretical Model of Legged Lander: Modeling and Analysis

In this paper, a novel 3D (three-dimensional) soft-landing dynamic theoretical model of a legged lander is developed in detail as well as its numerical solution process. The six degrees of freedom motion (6-DOF) of the base model of the lander with mass center offset setting is considered in the model as well as the spatial motion (3-DOF) of each landing gear. The characteristics of the buffering force, the footpad–ground contact, and the inter-structure friction are also taken into account during the motion of each landing gear. The direct constraint violation correction is used to control the constraint stabilization of the nonlinear dynamic equation. Comparative studies between the results from the proposed model and the simulated model (built in MSC Adams) under four classical load cases show the validity of the model. Additionally, the influences of different types of contact force models, friction force models, and a friction correction model used in the soft-landing dynamic model are further investigated as a step toward understanding the soft-landing dynamic performance and the feasibility of the dynamic model method of a legged lander. The results indicate that a precise lateral force model of the footpad–ground contact is necessary to obtain the soft-landing performance of one lander during soft landing

Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore

Xie G, Wang J, Liu J, Jin Y, Zheng F. Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore. Presented at the The Eleventh International Conference on Learning Representations (ICLR 2023)