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ChrSNet: Chromosome Straightening using Self-attention Guided Networks

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arXiv 2022年

作者： Zheng, Sunyi Li, Jingxiong Shui, Zhongyi Zhu, Chenglu Zhang, Yunlong Chen, Pingyi Yang, Lin Artificial Intelligence and Biomedical Image Analysis Lab School of Engineering Westlake University China Institute of Advanced Technology Westlake Institute for Advanced Study China College of Computer Science and Technology Zhejiang University China

Karyotyping is an important procedure to assess the possible existence of chromosomal abnormalities. However, because of the non-rigid nature, chromosomes are usually heavily curved in microscopic images and such deformed shapes hinder the chromosome analysis for cytogeneticists. In this paper, we present a self-attention guided framework to erase the curvature of chromosomes. The proposed framework extracts spatial information and local textures to preserve banding patterns in a regression module. With complementary information from the bent chromosome, a refinement module is designed to further improve fine details. In addition, we propose two dedicated geometric constraints to maintain the length and restore the distortion of chromosomes. To train our framework, we create a synthetic dataset where curved chromosomes are generated from the real-world straight chromosomes by grid-deformation. Quantitative and qualitative experiments are conducted on synthetic and real-world data. Experimental results show that our proposed method can effectively straighten bent chromosomes while keeping banding details and length. Copyright © 2022, The Authors. All rights reserved.

关键词： Chromosomes

WEAKLY SUPERVISED LEARNING FOR CELL RECOGNITION IN IMMUNOHISTOCHEMICAL CYTOPLASM STAINING imageS

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arXiv 2022年

作者： Zhang, Shichuan Zhu, Chenglu Li, Honglin Cai, Jiatong Yang, Lin Artificial Intelligence and Biomedical Image Analysis Lab School of Engineering Westlake University China Institute of Advanced Technology Westlake Institute for Advanced Study China College of Computer Science & Technology Zhejiang University China

Cell classification and counting in immunohistochemical cytoplasm staining images play a pivotal role in cancer diagnosis. Weakly supervised learning is a potential method to deal with labor-intensive labeling. However, the inconstant cell morphology and subtle differences between classes also bring challenges. To this end, we present a novel cell recognition framework based on multi-task learning, which utilizes two additional auxiliary tasks to guide robust representation learning of the main task. To deal with misclassification, the tissue prior learning branch is introduced to capture the spatial representation of tumor cells without additional tissue annotation. Moreover, dynamic masks and consistency learning are adopted to learn the invariance of cell scale and shape. We have evaluated our framework on immunohistochemical cytoplasm staining images, and the results demonstrate that our method outperforms recent cell recognition approaches. Besides, we have also done some ablation studies to show significant improvements after adding the auxiliary branches. Copyright © 2022, The Authors. All rights reserved.

关键词： Cytology

To Make Yourself Invisible with Adversarial Semantic Contours

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arXiv 2023年

作者： Zhang, Yichi Zhu, Zijian Su, Hang Zhu, Jun Zheng, Shibao He, Yuan Xue, Hui Department of Computer Science and Technology Institute for Artificial Intelligence THBI Lab Tsinghua University Beijing100084 China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China Alibaba Group Hangzhou311121 China

Modern object detectors are vulnerable to adversarial examples, which may bring risks to real-world applications. The sparse attack is an important task which, compared with the popular adversarial perturbation on the whole image, needs to select the potential pixels that is generally regularized by an 0-norm constraint, and simultaneously optimize the corresponding texture. The non-differentiability of 0 norm brings challenges and many works on attacking object detection adopted manually-designed patterns to address them, which are meaningless and independent of objects, and therefore lead to relatively poor attack performance. In this paper, we propose Adversarial Semantic Contour (ASC), an MAP estimate of a Bayesian formulation of sparse attack with a deceived prior of object contour. The object contour prior effectively reduces the search space of pixel selection and improves the attack by introducing more semantic bias. Extensive experiments demonstrate that ASC can corrupt the prediction of 9 modern detectors with different architectures (e.g., one-stage, two-stage and Transformer) by modifying fewer than 5% of the pixels of the object area in COCO in white-box scenario and around 10% of those in black-box scenario. We further extend the attack to datasets for autonomous driving systems to verify the effectiveness. We conclude with cautions about contour being the common weakness of object detectors with various architecture and the care needed in applying them in safety-sensitive scenarios. © 2023, CC BY.

关键词： Semantics

Mitosis detection and classification for breast cancer diagnosis: What we know and what is next

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Computers in Biology and Medicine 2025年 191卷 110057-110057页

作者： Khalil, Rafi Ullah Sajjad, Muhammad Dhahbi, Sami Bourouis, Sami Hijji, Mohammad Muhammad, Khan Digital Image Processing Lab Department of Computer Science Islamia College Peshawar Peshawar25000 Pakistan Applied College of Mahail Aseer King Khalid University Muhayil Aseer62529 Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Faculty of Computers and Information Technology University of Tabuk Tabuk71491 Saudi Arabia Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

Breast cancer is the second most deadly malignancy in women, behind lung cancer. Despite significant improvements in medical research, breast cancer is still accurately diagnosed with histological analysis. During this procedure, pathologists examine a physical sample for the presence of mitotic cells, or dividing cells. However, the high resolution of histopathology images and the difficulty of manually detecting tiny mitotic nuclei make it particularly challenging to differentiate mitotic cells from other types of cells. Numerous studies have addressed the detection and classification of mitosis, owing to increasing capacity and developments in automated approaches. The combination of machine learning and deep learning techniques has greatly revolutionized the process of identifying mitotic cells by offering automated, precise, and efficient solutions. In the last ten years, several pioneering methods have been presented, advancing towards practical applications in clinical settings. Unlike other forms of cancer, breast cancer and gliomas are categorized according to the number of mitotic divisions. Numerous papers have been published on techniques for identifying mitosis due to easy access to datasets and open competitions. Convolutional neural networks and other deep learning architectures can precisely identify mitotic cells, significantly decreasing the amount of labor that pathologists must perform. This article examines the techniques used over the past decade to identify and classify mitotic cells in histologically stained breast cancer hematoxylin and eosin images. Furthermore, we examine the benefits of current research techniques and predict forthcoming developments in the investigation of breast cancer mitosis, specifically highlighting machine learning and deep learning. © 2025 Elsevier Ltd

关键词： Lung cancer

SCSQ-MDD: A Detection Approach for Multi-Style Strokes of Chinese Characters

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SSRN

SSRN 2023年

作者： Zhou, Tian Xie, Wu Zhang, Huimin Fan, Yong Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin541004 China School of Mechanical and Electrical Engineering Guilin University of Electronic Science and Technology Guilin China

In the Chinese character writing task of the robotic arms, the stroke category and position information should be extracted by object detection. The detection algorithms based on predefined anchor frames have difficulty in resolving the differences among many different styles of Chinese character strokes. While the deformable detection transformer (deformable DETR) algorithms without predefined anchor frames result in some invalid sampling points having no contribution to the feature update of the current reference point due to the random sampling of sampling points in the deformable attention module. These processes cause the effectiveness of correlation calculations between reference points in Chinese strokes and their surrounding sampled points is limited. So that the speed of vector learning stroke features in the detection head is reduced. In view of this problem, a new detection method of multi-style strokes of Chinese characters via SCSQ-MDD (Simple Conditional Spatial Query Mask Deformable DETR) is proposed in this paper. Firstly, a mask prediction layer is jointly determined using the shallow feature map of the Chinese character image and the query vector of the transformer encoder, which is used to filter the points with actual contribution and resample the points without contribution, so that the randomness of correlation calculation among reference points is solved. Secondly, by separating the content query and spatial query of the transformer deocder, the content embedding and spatial embedding can be separately focused on when cross-attention computations are performed. Thus the dependence of the prediction task on the content embedding is relaxed and the training process is simplified. Finally, the detection model without predefined anchor frames based on deformable DETR called SCSQ-MDD is constructed using the mask mechanism and the simple conditional spatial query mechanism, and trained and validated on a multi-style Chinese character stroke dataset

关键词： Object detection

HNT-AI: An Automatic Segmentation Framework for Head and Neck Primary Tumors and Lymph Nodes in FDG- PET/CT images 3rd

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HNT-AI: An Automatic Segmentation Framework for Head and N...

3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th International Conference on Medical image Computing and Computer Assisted Intervention, MICCAI 2022

作者： Salahuddin, Zohaib Chen, Yi Zhong, Xian Rad, Nastaran Mohammadian Woodruff, Henry C. Lambin, Philippe The D-Lab Department of Precision Medicine GROW-School for Oncology and Reproduction Maastricht University Maastricht Netherlands Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis College of Computer Science and Technology Guizhou University Guiyang China Department of Medical Ultrasonics Institute of Diagnostic and Interventional Ultrasound The First Affiliated Hospital of Sun Yat-sen University Guangzhou China Department of Radiology and Nuclear Medicine GROW-School for Oncology Maastricht University Medical Center Maastricht Netherlands

ISBN: (纸本)9783031274190

Head and neck cancer is one of the most prevalent cancers in the world. Automatic delineation of primary tumors and lymph nodes is important for cancer diagnosis and treatment. In this paper, we develop a deep learning-based model for automatic tumor segmentation, HNT-AI, using PET/CT images provided by the MICCAI 2022 Head and Neck Tumor (HECKTOR) segmentation Challenge. We investigate the effect of residual blocks, squeeze-and-excitation normalization, and grid-attention gates on the performance of 3D-UNET. We project the predicted masks on the z-axis and apply k-means clustering to reduce the number of false positive predictions. Our proposed HNT-AI segmentation framework achieves an aggregated dice score of 0.774 and 0.759 for primary tumors and lymph nodes, respectively, on the unseen external test set. Qualitative analysis of the predicted segmentation masks shows that the predicted segmentation mask tends to follow the high standardized uptake value (SUV) area on the PET scans more closely than the ground truth masks. The largest tumor volume, the larget lymph node volume, and the total number of lymph nodes derived from the segmentation proved to be potential biomarkers for recurrence-free survival with a C-index of 0.627 on the test set. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Biomarkers

Coordinated Guiding Vector Field Design for Ordering-Flexible Multi-Robot Surface Navigation

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arXiv 2024年

作者： Hu, Bin-Bin Zhang, Hai-Tao Yao, Weijia Sun, Zhiyong Cao, Ming The School of Artificial Intelligence and Automation The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China The School of Robotics Hunan University Hunan410082 China The Institute of Engineering and Technology University of Groningen Groningen9747 AG Netherlands Department of Electrical Engineering Eindhoven University of Technology Netherlands

We design a distributed coordinated guiding vector field (CGVF) for a group of robots to achieve ordering-flexible motion coordination while maneuvering on a desired two-dimensional (2D) surface. The CGVF is characterized by three terms, i.e., a convergence term to drive the robots to converge to the desired surface, a propagation term to provide a traversing direction for maneuvering on the desired surface, and a coordinated term to achieve the surface motion coordination with an arbitrary ordering of the robotic group. By setting the surface parameters as additional virtual coordinates, the proposed approach eliminates potential singularity of the CGVF and enables both the global convergence to the desired surface and the maneuvering on the surface from all possible initial conditions. The ordering-flexible surface motion coordination is realized by each robot to share with its neighbors only two virtual coordinates, i.e. that of a given target and that of its own, which reduces the communication and computation cost in multi-robot surface navigation. Finally, the effectiveness of the CGVF is substantiated by extensive numerical simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Navigation

Multi-frame and multi-scale conditional generative adversarial networks for efficient foreground extraction 4th

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Multi-frame and multi-scale conditional generative adversari...

4th International Conference on Computer Vision and image Processing, CVIP 2019

作者： Didwania, Himansu Ghatak, Subhankar Rup, Suvendu Image and Video Processing Lab Department of Computer Science and Engineering International Institute of Information Technology Bhubaneswar Bhubaneswar751003 India

ISBN: (纸本)9789811540172

Alongside autonomous submissions, foreground extraction is considered to be the foundation for various video content analysis technologies, like moving object tracking, video surveillance and video summarization. This paper proposes an efficient foreground extraction methodology based on conditional Generative Adversarial Network. The proposed generator, which is made up of two networks working in series- Foreground Extractor and Segmentation Network, maps the video frames to corresponding foreground masks. The discriminator aids the learning of generator by learning to differentiate between seemingly real and fake foreground maps. The method used a multi-scale approach in order to capture robust features across multiple scales of input using the Feature Extractor Network, which are then used by the successive Segmentation Network to produce the final foreground map. In addition, a multi-frame approach is also used to facilitate capturing of appropriate temporal features. The performance of the proposed model is evaluated on CDnet 2014 Dataset and outperforms existing methods. © Springer Nature Singapore Pte Ltd 2020.

关键词： Generative adversarial networks

Grouped Adaptive Loss Weighting for Person Search

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arXiv 2022年

作者： Tian, Yanling Chen, Di Liu, Yunan Zhang, Shanshan Yang, Jian School of Artificial Intelligence Dalian Maritime University China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China

Person search is an integrated task of multiple sub-tasks such as foreground/background classification, bounding box regression and person re-identification. Therefore, person search is a typical multi-task learning problem, especially when solved in an end-to-end manner. Recently, some works enhance person search features by exploiting various auxiliary information, e.g. person joint key-points, body part position, attributes, etc., which brings in more tasks and further complexifies a person search model. The inconsistent convergence rate of each task could potentially harm the model optimization. A straightforward solution is to manually assign different weights to different tasks, compensating for the diverse convergence rates. However, given the special case of person search, i.e. with a large number of tasks, it is impractical to weight the tasks manually. To this end, we propose a Grouped Adaptive Loss Weighting (GALW) method which adjusts the weight of each task automatically and dynamically. Specifically, we group tasks according to their convergence rates. Tasks within the same group share the same learnable weight, which is dynamically assigned by considering the loss uncertainty. Experimental results on two typical benchmarks, CUHK-SYSU and PRW, demonstrate the effectiveness of our method. © 2022, CC BY.

关键词： Learning systems