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Structural Connectivity Enhanced Anisotropic 3D Network for Brain Midline Delineation

Journal of Beijing Institute of Technology 2023年第5期32卷 562-578页

作者： Yufan Liu Kongming Liang Yinuo Jing Shen Wang Zhanyu Ma Yiming Li Yizhou Yu Yizhou Wang Jun Guo Pattern Recognition and Intelligent System Laboratory School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijing 100876China. Center on Frontiers of Computing Studies School of Computer SciencePeking UniversityBeijing 100871China AI Lab Deepwise HealthcareBeijing 100089China

Brain midline delineation can facilitate the clinical evaluation of brain midline shift,which has a pivotal role in the diagnosis and prognosis of various brain ***,there are still challenges for brain midline delineation:1)the largely deformed midline is hard to localize if mixed with severe cerebral hemorrhage;2)the predicted midlines of recent methods are not smooth and continuous which violates the structural *** overcome these challenges,we propose an anisotropic three dimensional(3D)network with context-aware refinement(A3D-CAR)for brain midline *** proposed network fuses 3D context from different two dimensional(2D)slices through asymmetric context *** exploit the elongated structure of the midline,an anisotropic block is designed to balance the difference between the adjacent pixels in the horizontal and vertical *** maintaining the structural priority of a brain midline,we present a novel 3D connectivity regular loss(3D CRL)to penalize the disconnectivity between nearby *** experiments on the CQ dataset and one in-house dataset show that the proposed method outperforms three state-of-the-art methods on four evaluation metrics without excessive computational burden.

关键词： brain midline delineation refinement network structure prior connectivity regular loss

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Channel boosting based detection and segmentation for cancer analysis in histopathological images. 19

Channel boosting based detection and segmentation for cancer...

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19th International Bhurban Conference on Applied Sciences and Technology, IBCAST 2022

作者： Ali, Momina Liaqat Rauf, Zunaira Khan, Abdur Rehman Khan, Asifullah Pattern Recognotion Lab Department of Computer and Information Sciences PIEAS Islamabad45650 Pakistan Pattern Recognition Lab Department of Computer and Information Sciences Pakistan Artificial Intelligence Center PIEAS Islamabad45650 Pakistan

ISBN: (数字)9781665460514

ISBN: (纸本)9781665460514

Human immune system plays a vital role in cancer prevention. Tumor Infiltrating Lymphocytes (TILs) are specialized immune cells that infiltrate tumor region. Studies have shown that the presence of TILs reflect towards cancer prognosis. The amount of TILs in tumor stroma can be counted manually under a microscope by an expert pathologist. This manual task is very laborious and time-consuming, and may face human subjectivity. Therefore, an automated system is required to effectively detect lymphocytes and to overcome the burden on pathologist. Automation of lymphocyte detection is a challenging task, due to its morphological variations, clustered appearance and presence of artifacts. To address these challenges, we have designed a computer aided diagnostic system called Pyramid Vision Transformer Channel Boosted Lymphocyte Detection (PVTCB-Lymph-Det) by exploiting deep CNN architectures based on channel boosting. In this work, we have incorporated the idea of channel boosting to enhance the learning capacity of the proposed model. The proposed network is based on a transformer and Convolutional Block Attention Mechanism (CBAM) with ResNet-50 to perform feature extraction. The proposed technique with Channel Boosting has performed well for lymphocyte detection and has an F-score of 88.92%. Moreover, PVTCB-Lymph-Det can be adapted to detect lymphocytes and may aid pathologists in solving medical diagnostic problems. © 2022 IEEE.

关键词： Tumors

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Data driven analysis of particulate systems for development of reliable model to determine drag coefficient of non-spherical particles

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Particuology 2025年第2期97卷 219-235页

作者： Tauseeq Hussain Atta Ullah Rehan Zubair Khalid Farooq Ahmad Fei Li Asifullah Khan Department of Chemical Engineering Pakistan Institute of Engineering and Applied SciencesIslamabad45650Pakistan PIEAS Artificial Intelligence Center Pakistan Institute of Engineering and Applied SciencesNiloreIslamabad45650Pakistan Department of Chemical and Materials Engineering College of EngineeringNorthern Border UniversityArarSaudi Arabia Institute of Process Engineering Chinese Academy of SciencesBeijingChina Pattern Recognition Lab Department of Computer and Information SciencesPakistan Institute of Engineering and Applied SciencesNiloreIslamabad45650Pakistan

Non-spherical particles are extensively encountered in the process industry such as feedstock or catalysts e.g.,energy,food,pharmaceuticals,and *** design of equipment used to process these particles is highly dependent upon the accurate and reliable modeling of hydrodynamics of particulate media *** coefficient of these particles is the most significant of all parameters.A universal model to predict the drag coefficient of such particles has not yet been developed due to the diversity and complexity of particle shapes and *** this into consideration,we propose a unique approach to model the drag coefficient of non-spherical particles using machine learning(ML)to move towards generalization.A comprehensive database of approximately five thousand data points from reliable experiments and high-resolution simulations was compiled,covering a wide range of *** drag coefficient was modeled as a function of Reynolds number,sphericity,Corey Shape Factor,aspect ratio,volume fraction,and angle of *** ML techniques—artificial Neural Networks,Random Forest,and AdaBoost—were used to train the *** models demonstrated strong generalization when tested on unseen ***,AdaBoost outperformed the others with the lowest MAPE(20.1%)and MRD(0.069).Additional analysis on excluded data confirmed the robust predictive abilities and generalization of the proposed *** models were also evaluated across three flow regimes—Stokes,transitional,and turbulent—to further assess their generalization.A comparative analysis with well-known empirical correlations,such as Haider and Levenspiel and Chien,showed that all ML models outperformed traditional approaches,with AdaBoost achieving the best *** current work demonstrates that new generated ML techniques can be reliably used to predict drag coefficient of non-spherical particles paving way towards generalization of ML approach.

关键词： Drag coefficient Non-spherical Neural network Random forest AdaBoost

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Mclabel: A Local Thresholding Tool for Efficient Semi-automatic labelling of Cells in Fluorescence Microscopy

McLabel: A Local Thresholding Tool for Efficient Semi-automa...

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Bildverarbeitung für die Medizin Workshop, BVM 2023

作者： Utz, Jonas Schlereth, Maja Qiu, Jingna Thies, Mareike Wagner, Fabian Brahim, Oumaima B. Gu, Mingxuan Uderhardt, Stefan Breininger, Katharina Department Artificial Intelligence in Biomedical Engineering FAU Erlangen-Nürnberg Germany Pattern Recognition Lab FAU Erlangen-Nürnberg Germany Department of Medicine 3 - Rheumatology and Immunology FAU Erlangen-Nürnberg Germany

ISBN: (纸本)9783658416560

In this work, we present a semi-automatic labelling tool for the annotation of complex cellular structures such as macrophages in fluorescence microscopy images. We present Mclabel, a napari plugin that allows users to label structures of interest by simply scribbling outlines around the area of interest, using the triangle thresholding method with post-processing to identify the desired structure. Additionally, manual adaption of the threshold allows for quick and fine-grained local correction of the segmentation. The tool is evaluated in a user study with five experts, who annotated images both with and without the tool. The results show that variability in annotations between experts is reduced when the labelling tool is used and annotation time is reduced by a factor of five on average1. © 2023 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature.

关键词： Fluorescence microscopy

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Multi-Feature Super-Resolution Network for Cloth Wrinkle Synthesis

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Journal of Computer Science & Technology 2021年第3期36卷 478-493页

作者： Lan Chen Juntao Ye Xiaopeng Zhang National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Zhejiang Lab Hangzhou 311121China

Existing physical cloth simulators suffer from expensive computation and difficulties in tuning mechanical parameters to get desired wrinkling ***-driven methods provide an alternative *** typically synthesize cloth animation at a much lower computational cost,and also create wrinkling effects that are similar to the training *** this paper we propose a deep learning based method for synthesizing cloth animation with high resolution *** do this we first create a dataset for training:a pair of low and high resolution meshes are simulated and their motions are *** a result the two meshes exhibit similar large-scale deformation but different small *** simulated mesh pair is then converted into a pair of low-and high-resolution"images"(a 2D array of samples),with each image pixel being interpreted as any of three descriptors:the displacement,the normal and the *** these image pairs,we design a multi-feature super-resolution(MFSR)network that jointly trains an upsampling synthesizer for the three *** MFSR architecture consists of shared and task-specific layers to learn multi-level features when super-resolving three descriptors ***-to-frame consistency is well maintained thanks to the proposed kinematics-based loss *** method achieves realistic results at high frame rates:12-14 times faster than traditional physical *** demonstrate the performance of our method with various experimental scenes,including a dressed character with sophisticated collisions.

关键词： cloth animation deep learning multi-feature super-resolution wrinkle synthesis

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Effective and Robust Detection of Adversarial Examples via Benford-Fourier Coefficients

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Machine intelligence Research 2023年第5期20卷 666-682页

作者： Cheng-Cheng Ma Bao-Yuan Wu Yan-Bo Fan Yong Zhang Zhi-Feng Li National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China School of Data Science The Chinese University of Hong KongShenzhen 518172China Shenzhen Research Institute of Big Data Shenzhen 518172China AI Lab Tencent Inc.Shenzhen 518057China

Adversarial example has been well known as a serious threat to deep neural networks(DNNs).In this work,we study the detection of adversarial examples based on the assumption that the output and internal responses of one DNN model for both adversarial and benign examples follow the generalized Gaussian distribution(GGD)but with different parameters(i.e.,shape factor,mean,and variance).GGD is a general distribution family that covers many popular distributions(e.g.,Laplacian,Gaussian,or uniform).Therefore,it is more likely to approximate the intrinsic distributions of internal responses than any specific ***,since the shape factor is more robust to different databases rather than the other two parameters,we propose to construct discriminative features via the shape factor for adversarial detection,employing the magnitude of Benford-Fourier(MBF)coefficients,which can be easily estimated using ***,a support vector machine is trained as an adversarial detector leveraging the MBF *** experiments in terms of image classification demonstrate that the proposed detector is much more effective and robust in detecting adversarial examples of different crafting methods and sources compared to state-of-the-art adversarial detection methods.

关键词： Adversarial defense adversarial detection generalized Gaussian distribution Benford-Fourier coefficients image classification

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Reference Algorithms for the Mitosis Domain Generalization (MIDOG) 2022 Challenge 25th

Reference Algorithms for the Mitosis Domain Generalization...

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25th International Conference on Medical Image Computing and Computer-Assisted Intervention , MICCAI 2022

作者： Ammeling, Jonas Wilm, Frauke Ganz, Jonathan Breininger, Katharina Aubreville, Marc Technische Hochschule Ingolstadt Ingolstadt Germany Pattern Recognition Lab Computer Sciences Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

ISBN: (纸本)9783031336577

Robust mitosis detection on images from different tumor types, pathology labs, and species is a challenging task that was addressed in the MICCAI Mitosis Domain Generalization (MIDOG) 2022 challenge. In this work, we describe three reference algorithms that were provided as a baseline for the challenge: A Mask-RCNN-based instance segmentation model trained on the MIDOG 2022 dataset, and two different versions of the domain-adversarial RetinaNet which already served as the baseline for MIDOG 2021 challenge, one trained on the MIDOG 2022 dataset and the other trained only on human breast carcinoma from MIDOG 2021. The domain-adversarial RetinaNet trained on the MIDOG 2022 dataset had the highest F 1 score of 0.7135 on the final test set. When trained on breast carcinoma only, the same network had a much lower F 1 score of 0.4719, indicating a significant domain shift between mitotic figure and tissue representation in different tumor types. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Tumors

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Unpaired Image-Text Matching via Multimodal Aligned Conceptual Knowledge

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IEEE Transactions on pattern Analysis and Machine intelligence 2024年第7期47卷 5160-5176页

作者： Yan Huang Yuming Wang Yunan Zeng Junshi Huang Zhenhua Chai Liang Wang New Laboratory of Pattern Recognition (NLPR) State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS) Institute of Automation Chinese Academy of Sciences (CASIA) Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences (UCAS) Beijing China AI Lab Meituan Beijing China

Recently, the accuracy of image-text matching has been greatly improved by multimodal pretrained models, all of which use millions or billions of paired images and texts for supervised model learning. Different from them, human brains can well match images with texts using their stored multimodal knowledge. Inspired by that, this paper studies a new scenario as unpaired image-text matching, in which paired images and texts are assumed to be unavailable during model learning. To deal with it, we accordingly propose a simple yet effective method namely Multimodal Aligned Conceptual Knowledge (MACK). First, we collect a set of words and their related image regions from publicly available datasets, and compute prototypical region representations to obtain pretrained general knowledge. To make the obtained knowledge better suit for certain datasets, we refine it using unpaired images and texts in a self-supervised learning manner to obtain fine-tuned domain knowledge. Then, to match given images with texts based on the knowledge, we represent parsed words in the texts by prototypical region representations, and compute region-word similarity scores. At last, the scores are aggregated based on bidirectional similarity pooling into an image-text similarity score, which can be directly used for unpaired image-text matching. The proposed MACK is complementary with existing models, which can be easily extended as a re-ranking method to substantially improve their performance of zero-shot and cross-dataset image-text matching.

关键词： Visualization Semantics Task analysis Knowledge graphs Computational modeling Brain modeling Supervised learning

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Lung, Nodule and Airway Segmentation Using Partially Annotated Data

Lung, Nodule and Airway Segmentation Using Partially Annotat...

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Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling

作者： von Querfurth, Anne Kordon, Florian Denzinger, Felix Breininger, Katharina Kunze, Holger Department Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Siemens Healthcare GmbH Forchheim Germany Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

ISBN: (纸本)9781510660373

To support the development of an automatic path-planning procedure for bronchoscopy, semantic segmentation of pulmonary nodules and airways is required. The segmentation should happen simultaneously and automatically to save time and effort during the intervention. The challenges of the combined segmentation are the different shapes, frequencies, and sizes of airways, lungs, and pulmonary nodules. Therefore, a sampling strategy is explored using especially relevant crops of the volumes during training and weighting the classes differently, counteracting class imbalance. For the segmentation, a 3D U-Net is used. The proposed algorithm is compared to nnU-Net. First, it is trained as a one-class problem on all classes individually and in a second approach as a multi-label problem. The developed multi-label segmentation network (MLS) is trained with full supervision. The results of the experiments have shown that without further adaption, a combined segmentation of nodules, airways, and lungs is complex. The multi-label nnU-Net failed to find nodules. Considering the different properties of the three classes, MLS accomplishes segmenting all classes simultaneously. © 2023 SPIE.

关键词： Motion planning

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CB-UNet: A Channel Boosted UNet Architecture for Lymphocyte Detection in Histopathological Images

CB-UNet: A Channel Boosted UNet Architecture for Lymphocyte ...

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International Bhurban Conference on Applied Sciences & Technology, IBCAST

作者： Linta Kamran Hajra Kamal Zunaira Rauf Asifullah Khan Department of Computer and Information Sciences Pattern Recognotion Lab PIEAS Islamabad Pakistan Department of Computer and Information Sciences Pattern Recognition Lab PIEAS Islamabad Pakistan Department of Computer and Information Sciences Pattern Recognition Lab Pakistan Artificial Intelligence Center PIEAS Islamabad Pakistan

ISBN: (数字)9798350308259

ISBN: (纸本)9798350308266

Accurate identification and segmentation of cancer cell nuclei from histopathological images is of vital importance in efficient cancer diagnosis and treatment. Problems in manual segmentation raised the need for automated lymphocyte detection as manual techniques are time-consuming, error-prone, and are more subjected to pathologists conflict. Much work have been done previously in automated detection of lymphocyte. In this study, we propose a unique architecture called CB-UNet for nuclei segmentation in cancer histopathology images. Our approach, CB-UNet utilizes the concept of “channel boosting,” employing multiple encoders to enhance the input channels and generate diverse and informative feature maps. By merging these feature maps and passing them through a decoder layer, we generate high-resolution segmentation masks. We evaluated our model's performance using evaluation metrics such as Intersection over Union (IOU) and Dice coefficient on the Lysto dataset and the MonuSeg dataset. These metrics demonstrate the superiority of our proposed architecture, CB-UNet, over existing methods. The results highlight the enhanced precision and effectiveness of nuclei segmentation for cancer diagnosis and treatment. This study signifies an advancement in nuclei segmentation techniques, with the potential to improve the overall treatment of cancer patients.

关键词： Measurement Image segmentation Accuracy Histopathology Microprocessors Merging Computer architecture Manuals Decoding Cancer

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