arXiv subjects

Chenxi Zhang

Publications and source records attributed to Chenxi Zhang.

29 records · Page 2Linked to original sources

arXiv2024

An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution

High-quality whole-slide scanning is expensive, complex, and time-consuming, thus limiting the acquisition and utilization of high-resolution histopathology images in daily clinical work. Deep learning-based single-image super-resolution (SISR) techniques provide an effective way to solve this problem. However, the existing SISR models applied in histopathology images can only work in fixed integer scaling factors, decreasing their applicability. Though methods based on implicit neural representation (INR) have shown promising results in arbitrary-scale super-resolution (SR) of natural images, applying them directly to histopathology images is inadequate because they have unique fine-grained image textures different from natural images. Thus, we propose an Implicit Self-Texture Enhancement-based dual-branch framework (ISTE) for arbitrary-scale SR of histopathology images to address this challenge. The proposed ISTE contains a feature aggregation branch and a texture learning branch. We employ the feature aggregation branch to enhance the learning of the local details for SR images while utilizing the texture learning branch to enhance the learning of high-frequency texture details. Then, we design a two-stage texture enhancement strategy to fuse the features from the two branches to obtain the SR images. Experiments on publicly available datasets, including TMA, HistoSR, and the TCGA lung cancer datasets, demonstrate that ISTE outperforms existing fixed-scale and arbitrary-scale SR algorithms across various scaling factors. Additionally, extensive experiments have shown that the histopathology images reconstructed by the proposed ISTE are applicable to downstream pathology image analysis tasks.

eess.IV↗

arXiv2024

O-PRESS: Boosting OCT axial resolution with Prior guidance, Recurrence, and Equivariant Self-Supervision

Optical coherence tomography (OCT) is a noninvasive technology that enables real-time imaging of tissue microanatomies. The axial resolution of OCT is intrinsically constrained by the spectral bandwidth of the employed light source while maintaining a fixed center wavelength for a specific application. Physically extending this bandwidth faces strong limitations and requires a substantial cost. We present a novel computational approach, called as O-PRESS, for boosting the axial resolution of OCT with Prior Guidance, a Recurrent mechanism, and Equivariant Self-Supervision. Diverging from conventional superresolution methods that rely on physical models or data-driven techniques, our method seamlessly integrates OCT modeling and deep learning, enabling us to achieve real-time axial-resolution enhancement exclusively from measurements without a need for paired images. Our approach solves two primary tasks of resolution enhancement and noise reduction with one treatment. Both tasks are executed in a self-supervised manner, with equivariance imaging and free space priors guiding their respective processes. Experimental evaluations, encompassing both quantitative metrics and visual assessments, consistently verify the efficacy and superiority of our approach, which exhibits performance on par with fully supervised methods. Importantly, the robustness of our model is affirmed, showcasing its dual capability to enhance axial resolution while concurrently improving the signal-to-noise ratio.

eess.IV↗

arXiv2023

keqing: knowledge-based question answering is a nature chain-of-thought mentor of LLM

Large language models (LLMs) have exhibited remarkable performance on various natural language processing (NLP) tasks, especially for question answering. However, in the face of problems beyond the scope of knowledge, these LLMs tend to talk nonsense with a straight face, where the potential solution could be incorporating an Information Retrieval (IR) module and generating response based on these retrieved knowledge. In this paper, we present a novel framework to assist LLMs, such as ChatGPT, to retrieve question-related structured information on the knowledge graph, and demonstrate that Knowledge-based question answering (Keqing) could be a nature Chain-of-Thought (CoT) mentor to guide the LLM to sequentially find the answer entities of a complex question through interpretable logical chains. Specifically, the workflow of Keqing will execute decomposing a complex question according to predefined templates, retrieving candidate entities on knowledge graph, reasoning answers of sub-questions, and finally generating response with reasoning paths, which greatly improves the reliability of LLM's response. The experimental results on KBQA datasets show that Keqing can achieve competitive performance and illustrate the logic of answering each question.

cs.CL↗

arXiv2023

The 2023 Development of Room-Temperature Ambient-Pressure Superconductor: Vision and Future Trend of Power Systems

Room-Temperature Ambient-Pressure Superconductor (RTAPS) can achieve superconducting properties at room temperature and normal atmospheric pressure, eliminating the power system's transmission loss and enhancing power systems efficiency. This paper investigates the comprehensive implications and prospective applications of the recently discovered RTAPS, LK-99, in modern power systems. It explores the potential of RTAPS in reshaping modern power systems paradigms, providing the vision of future RTAPS-based power systems. Although debate surrounds its industrial implementations, RTAPS's benefits for electricity transmission, grid flexibility, improved energy storage, and renewable energy integration could be unprecedented. The paper delves into underlying opportunities and challenges, including RTAPS-based power flow methods evolution, security redefinition, computational efficiency, cost implications, and innovative market transaction forms to facilitate renewable energy competitiveness.

physics.soc-ph↗

arXiv2023

Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation

Precise delineation of multiple organs or abnormal regions in the human body from medical images plays an essential role in computer-aided diagnosis, surgical simulation, image-guided interventions, and especially in radiotherapy treatment planning. Thus, it is of great significance to explore automatic segmentation approaches, among which deep learning-based approaches have evolved rapidly and witnessed remarkable progress in multi-organ segmentation. However, obtaining an appropriately sized and fine-grained annotated dataset of multiple organs is extremely hard and expensive. Such scarce annotation limits the development of high-performance multi-organ segmentation models but promotes many annotation-efficient learning paradigms. Among these, studies on transfer learning leveraging external datasets, semi-supervised learning using unannotated datasets and partially-supervised learning integrating partially-labeled datasets have led the dominant way to break such dilemma in multi-organ segmentation. We first review the traditional fully supervised method, then present a comprehensive and systematic elaboration of the 3 abovementioned learning paradigms in the context of multi-organ segmentation from both technical and methodological perspectives, and finally summarize their challenges and future trends.

eess.IV↗

arXiv2022

Robust Point Cloud Registration Framework Based on Deep Graph Matching(TPAMI Version)

3D point cloud registration is a fundamental problem in computer vision and robotics. Recently, learning-based point cloud registration methods have made great progress. However, these methods are sensitive to outliers, which lead to more incorrect correspondences. In this paper, we propose a novel deep graph matching-based framework for point cloud registration. Specifically, we first transform point clouds into graphs and extract deep features for each point. Then, we develop a module based on deep graph matching to calculate a soft correspondence matrix. By using graph matching, not only the local geometry of each point but also its structure and topology in a larger range are considered in establishing correspondences, so that more correct correspondences are found. We train the network with a loss directly defined on the correspondences, and in the test stage the soft correspondences are transformed into hard one-to-one correspondences so that registration can be performed by a correspondence-based solver. Furthermore, we introduce a transformer-based method to generate edges for graph construction, which further improves the quality of the correspondences. Extensive experiments on object-level and scene-level benchmark datasets show that the proposed method achieves state-of-the-art performance. The code is available at: \href{https://github.com/fukexue/RGM}{https://github.com/fukexue/RGM}.

cs.CV↗

arXiv2021

MicroHECL: High-Efficient Root Cause Localization in Large-Scale Microservice Systems

Availability issues of industrial microservice systems (e.g., drop of successfully placed orders and processed transactions) directly affect the running of the business. These issues are usually caused by various types of service anomalies which propagate along service dependencies. Accurate and high-efficient root cause localization is thus a critical challenge for large-scale industrial microservice systems. Existing approaches use service dependency graph based analysis techniques to automatically locate root causes. However, these approaches are limited due to their inaccurate detection of service anomalies and inefficient traversing of service dependency graph. In this paper, we propose a high-efficient root cause localization approach for availability issues of microservice systems, called MicroHECL. Based on a dynamically constructed service call graph, MicroHECL analyzes possible anomaly propagation chains, and ranks candidate root causes based on correlation analysis. We combine machine learning and statistical methods and design customized models for the detection of different types of service anomalies (i.e., performance, reliability, traffic). To improve the efficiency, we adopt a pruning strategy to eliminate irrelevant service calls in anomaly propagation chain analysis. Experimental studies show that MicroHECL significantly outperforms two state-of-the-art baseline approaches in terms of both accuracy and efficiency. MicroHECL has been used in Alibaba and achieves a top-3 hit ratio of 68% with root cause localization time reduced from 30 minutes to 5 minutes.

cs.SE↗

arXiv2021

Colorectal Polyp Detection in Real-world Scenario: Design and Experiment Study

Colorectal polyps are abnormal tissues growing on the intima of the colon or rectum with a high risk of developing into colorectal cancer, the third leading cause of cancer death worldwide. Early detection and removal of colon polyps via colonoscopy have proved to be an effective approach to prevent colorectal cancer. Recently, various CNN-based computer-aided systems have been developed to help physicians detect polyps. However, these systems do not perform well in real-world colonoscopy operations due to the significant difference between images in a real colonoscopy and those in the public datasets. Unlike the well-chosen clear images with obvious polyps in the public datasets, images from a colonoscopy are often blurry and contain various artifacts such as fluid, debris, bubbles, reflection, specularity, contrast, saturation, and medical instruments, with a wide variety of polyps of different sizes, shapes, and textures. All these factors pose a significant challenge to effective polyp detection in a colonoscopy. To this end, we collect a private dataset that contains 7,313 images from 224 complete colonoscopy procedures. This dataset represents realistic operation scenarios and thus can be used to better train the models and evaluate a system's performance in practice. We propose an integrated system architecture to address the unique challenges for polyp detection. Extensive experiments results show that our system can effectively detect polyps in a colonoscopy with excellent performance in real time.

cs.CV↗

arXiv2019

AFP-Net: Realtime Anchor-Free Polyp Detection in Colonoscopy

Colorectal cancer (CRC) is a common and lethal disease. Globally, CRC is the third most commonly diagnosed cancer in males and the second in females. For colorectal cancer, the best screening test available is the colonoscopy. During a colonoscopic procedure, a tiny camera at the tip of the endoscope generates a video of the internal mucosa of the colon. The video data are displayed on a monitor for the physician to examine the lining of the entire colon and check for colorectal polyps. Detection and removal of colorectal polyps are associated with a reduction in mortality from colorectal cancer. However, the miss rate of polyp detection during colonoscopy procedure is often high even for very experienced physicians. The reason lies in the high variation of polyp in terms of shape, size, textural, color and illumination. Though challenging, with the great advances in object detection techniques, automated polyp detection still demonstrates a great potential in reducing the false negative rate while maintaining a high precision. In this paper, we propose a novel anchor free polyp detector that can localize polyps without using predefined anchor boxes. To further strengthen the model, we leverage a Context Enhancement Module and Cosine Ground truth Projection. Our approach can respond in real time while achieving state-of-the-art performance with 99.36% precision and 96.44% recall.

eess.IV↗

arXiv2019

3D Anchor-Free Lesion Detector on Computed Tomography Scans

Lesions are injuries and abnormal tissues in the human body. Detecting lesions in 3D Computed Tomography (CT) scans can be time-consuming even for very experienced physicians and radiologists. In recent years, CNN based lesion detectors have demonstrated huge potentials. Most of current state-of-the-art lesion detectors employ anchors to enumerate all possible bounding boxes with respect to the dataset in process. This anchor mechanism greatly improves the detection performance while also constraining the generalization ability of detectors. In this paper, we propose an anchor-free lesion detector. The anchor mechanism is removed and lesions are formalized as single keypoints. By doing so, we witness a considerable performance gain in terms of both accuracy and inference speed compared with the anchor-based baseline

cs.CV↗

arXiv2016

Covalent Nitrogen Doping and Compressive Strain in MoS2 by Remote N2 Plasma Exposure

Controllable doping of two-dimensional materials is highly desired for ideal device performance in both hetero- and p-n homo-junctions. Herein, we propose an effective strategy for doping of MoS2 with nitrogen through a remote N2 plasma surface treatment. By monitoring the surface chemistry of MoS2 upon N2 plasma exposure using in-situ X-ray photoelectron spectroscopy, we identified the presence of covalently bonded nitrogen in MoS2, where substitution of the chalcogen sulfur by nitrogen is determined as the doping mechanism. Furthermore, the electrical characterization demonstrates that p-type doping of MoS2 is achieved by nitrogen doping, in agreement with theoretical predictions. Notably, we found that the presence of nitrogen can induce compressive strain in the MoS2 structure, which represents the first evidence of strain induced by substitutional doping in a transition metal dichalcogenide material. Finally, our first principle calculations support the experimental demonstration of such strain, and a correlation between nitrogen doping concentration and compressive strain in MoS2 is elucidated.

cond-mat.mtrl-sci↗