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arXiv 2023年

作者： Ali, Momina Liaqat Rauf, Zunaira Khan, Asifullah Sohail, Anabia Ullah, Rafi Gwak, Jeonghwan Pattern Recognition Lab Department of Computer & Information Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Center for Mathematical Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Department of Computer Science Faculty of Computing and Artificial Intelligence Air University IslamabadE-9 Pakistan Perak31750 Malaysia Department of Software Korea National University of Transportation Chungju27469 Korea Republic of

Transformers, due to their ability to learn long-range dependencies, have overcome the shortcomings of convolutional neural networks (CNNs) for global perspective learning. However, their multi-head attention module only captures global-level feature representations, which is insufficient for medical images. To address this issue, we propose a Channel Boosted Hybrid Vision Transformer (CB-HVT) that uses transfer learning to generate boosted channels and employs both transformers and CNNs to analyse lymphocytes in histopathological images. The proposed CB-HVT comprises five modules to effectively identify lymphocytes. Its (i) channel generation module, uses the idea of channel boosting through transfer learning to extract diverse channels from different auxiliary learners. These boosted channels are first concatenated and ranked using an attention mechanism in the (ii) channel exploitation module. A fusion block is then utilized in the (iii) channel merging module for a gradual and systematic merging of the diverse boosted channels to improve the network’s learning representations. The CB-HVT also employs a proposal network in its (iv) region aware module followed by its (v) detection and segmentation head to accurately identify and distinguish objects, even in the regions with crowded presence and artifacts. We evaluated the proposed CB-HVT on two publicly available datasets for lymphocyte assessment in histopathological images. The results demonstrate that CB-HVT has a good generalization on unseen data therefore, it can serve as a valuable tool for pathologists. Copyright © 2023, The Authors. All rights reserved.

关键词： Lymphocytes

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UniVIP: A Unified Framework for Self-Supervised Visual Pre-training

arXiv

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arXiv 2022年

作者： Li, Zhaowen Zhu, Yousong Yang, Fan Li, Wei Zhao, Chaoyang Chen, Yingying Chen, Zhiyang Xie, Jiahao Wu, Liwei Zhao, Rui Tang, Ming Wang, Jinqiao National Laboratory Of Pattern Recognition Institute Of Automation CAS Beijing China School Of Artificial Intelligence University Of Chinese Academy Of Sciences Beijing China SenseTime Research Development Research Institute Of Guangzhou Smart City Guangzhou China S-Lab Nanyang Technological University Singapore Peng Cheng Laboratory Shenzhen China Qing Yuan Research Institute Shanghai Jiao Tong University Shanghai China

Self-supervised learning (SSL) holds promise in leveraging large amounts of unlabeled data. However, the success of popular SSL methods has limited on single-centricobject images like those in ImageNet and ignores the correlation among the scene and instances, as well as the semantic difference of instances in the scene. To address the above problems, we propose a Unified Self-supervised Visual Pre-training (UniVIP), a novel self-supervised framework to learn versatile visual representations on either single-centric-object or non-iconic dataset. The framework takes into account the representation learning at three levels: 1) the similarity of scene-scene, 2) the correlation of scene-instance, 3) the discrimination of instanceinstance. During the learning, we adopt the optimal transport algorithm to automatically measure the discrimination of instances. Massive experiments show that Uni-VIP pre-trained on non-iconic COCO achieves state-of-the-art transfer performance on a variety of downstream tasks, such as image classification, semi-supervised learning, object detection and segmentation. Furthermore, our method can also exploit single-centric-object dataset such as ImageNet and outperforms BYOL by 2.5% with the same pre-training epochs in linear probing, and surpass current self-supervised object detection methods on COCO dataset, demonstrating its universality and potential. © 2022, CC BY.

关键词： Object recognition

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Convergent Neuroimaging and Molecular Signatures in Mild Cognitive Impairment and Alzheimer’s Disease:A Data-Driven Meta-Analysis with N=3,118

引用

Neuroscience Bulletin 2024年第9期40卷 1274-1286页

作者： Xiaopeng Kang Dawei Wang Jiaji Lin Hongxiang Yao Kun Zhao Chengyuan Song Pindong Chen Yida Qu Hongwei Yang Zengqiang Zhang Bo Zhou Tong Han Zhengluan Liao Yan Chen Jie Lu Chunshui Yu Pan Wang Xinqing Zhang Ming Li Xi Zhang Tianzi Jiang Yuying Zhou Bing Liu Ying Han Yong Liu The Alzheimer’s Disease Neuroimaging Initiative The Multi-Center Alzheimer’s Disease Imaging(MCADI)Consortium School of Artificial Intelligence University of Chinese Academy of SciencesBeijing100049China Brainnetome Center and National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing100190China Department of Radiology Qilu Hospital of Shandong UniversityJi’nan250063China Department of Neurology the Second Affiliated Hospital of Air Force Medical UniversityXi’an710032China Department of Radiology Chinese PLA General HospitalBeijing100853China Department of Radiology the Second Medical CentreNational Clinical Research Centre for Geriatric DiseasesChinese PLA General HospitalBeijing100853China School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing100191China Department of Neurology Qilu Hospital of Shandong UniversityJi’nan250063China Department of Radiology Xuanwu Hospital of Capital Medical UniversityBeijing100053China Branch of Chinese PLA General HospitalSanya572013China Department of Neurology the Second Medical CentreNational Clinical Research Centre for Geriatric DiseasesChinese PLA General HospitalBeijing100853China Department of Radiology Tianjin Huanhu HospitalTianjin300222China Department of Psychiatry People’s Hospital of Hangzhou Medical CollegeZhejiang Provincial People’s HospitalHangzhou310014China Department of Radiology Tianjin Medical University General HospitalTianjin300070China Department of Neurology Tianjin Huanhu HospitalTianjin300222China Department of Neurology Xuanwu Hospital of Capital Medical UniversityBeijing100053China Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences&Yunnan Province Kunming Institute of ZoologyChinese Academy of SciencesKunming650201YunnanChina State Key Lab of Cognition Neuroscience&Learning Beijing Normal UniversityBeijing100875China National Clinical Research Center for Geriatric Disorders Beijing100053China Center of Alzheimer’s Disease Beijing Institute for Brain DisordersBeijing1000

The current study aimed to evaluate the susceptibility to regional brain atrophy and its biological mechanism in Alzheimer’s disease(AD).We conducted data-driven meta-analyses to combine 3,118 structural magnetic resonance images from three datasets to obtain robust atrophy *** we introduced a set of radiogenomic analyses to investigate the biological basis of the atrophy patterns in *** results showed that the hippocampus and amygdala exhibit the most severe atrophy,followed by the temporal,frontal,and occipital lobes in mild cognitive impairment(MCI)and *** extent of atrophy in MCI was less severe than that in AD.A series of biological processes related to the glutamate signaling pathway,cellular stress response,and synapse structure and function were investigated through gene set enrichment *** study contributes to understanding the manifestations of atrophy and a deeper understanding of the pathophysiological processes that contribute to atrophy,providing new insight for further clinical research on AD.

关键词： Alzheimer’s disease Structural magnetic resonance imaging Meta-analysis Brain atrophy Gene set enrichment analysis

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Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset

arXiv

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arXiv 2022年

作者： Wilm, Frauke Fragoso, Marco Marzahl, Christian Qiu, Jingna Puget, Chloé Diehl, Laura Bertram, Christof A. Klopfleisch, Robert Maier, Andreas Breininger, Katharina Aubreville, Marc Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Institute of Veterinary Pathology Freie Universität Berlin Berlin Germany Department Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Institute of Pathology University of Veterinary Medicine Vienna Austria Technische Hochschule Ingolstadt Ingolstadt Germany

Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging. Recently, deep learning-based approaches have proven their potential for supporting pathologists in this regard. However, many of these supervised algorithms require a large amount of annotated data for robust development. We present a publicly available dataset of 350 whole slide images of seven different canine cutaneous tumors complemented by 12,424 polygon annotations for 13 histologic classes, including seven cutaneous tumor subtypes. In inter-rater experiments, we show a high consistency of the provided labels, especially for tumor annotations. We further validate the dataset by training a deep neural network for the task of tissue segmentation and tumor subtype classification. We achieve a class-averaged Jaccard coefficient of 0.7047, and 0.9044 for tumor in particular. For classification, we achieve a slide-level accuracy of 0.9857. Since canine cutaneous tumors possess various histologic homologies to human tumors the added value of this dataset is not limited to veterinary pathology but extends to more general fields of application. Copyright © 2022, The Authors. All rights reserved.

关键词： Tumors

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First Steps on Gamification of Lung Fluid Cells Annotations in the Flower Domain

arXiv

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arXiv 2021年

作者： Kunzmann, Sonja Marzahl, Christian Denzinger, Felix Bertram, Christof Klopfleisch, Robert Breininger, Katharina Christlein, Vincent Maier, Andreas Pattern Recognition Lab FAU Erlangen-Nürnberg Erlangen Germany Department Artificial Intelligence in Biomedical Engineering FAU Erlangen-Nürnberg Erlangen Germany Institute of Pathology University of Verterinary Medicine Vienna Austria Institute of Veterinary Pathology Freie Universität Berlin Germany

Annotating data, especially in the medical domain, requires expert knowledge and a lot of effort. This limits the amount and/or usefulness of available medical data sets for experimentation. Therefore, developing strategies to increase the number of annotations while lowering the needed domain knowledge is of interest. A possible strategy is the use of gamification, i.e. transforming the annotation task into a game. We propose an approach to gamify the task of annotating lung fluid cells from pathological whole slide images (WSIs). As the domain is unknown to non-expert annotators, we transform images of cells to the domain of flower images using a CycleGAN architecture. In this more assessable domain, non-expert annotators can be (t)asked to annotate different kinds of flowers in a playful setting. In order to provide a proof of concept, this work shows that the domain transfer is possible by evaluating an image classification network trained on real cell images and tested on the cell images generated by the CycleGAN network (reconstructed cell images) as well as real cell images. The classification network reaches an average accuracy of 94.73 % on the original lung fluid cells and 95.25 % on the transformed lung fluid cells, respectively. Our study lays the foundation for future research on gamification using CycleGANs. © 2021, CC BY-NC-ND.

关键词： Biological organs

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Initial Investigations Towards Non-invasive Monitoring of Chronic Wound Healing Using Deep Learning and Ultrasound Imaging

arXiv

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arXiv 2022年

作者： Schlereth, Maja Stromer, Daniel Mantri, Yash Tsujimoto, Jason Breininger, Katharina Maier, Andreas Anderson, Caesar Garimella, Pranav S. Jokerst, Jesse V. Department Artificial Intelligence in Biomedical Engineering FAU Erlangen-Nürnberg Erlangen Germany Pattern Recognition Lab FAU Erlangen-Nürnberg Erlangen Germany Department of Bioengineering University of California San Diego United States Department of Emergency Medicine San Diego United States Division of Nephrology and Hypertension Department of Medicine San Diego United States Department of Nanoengineering University of California San Diego United States

Chronic wounds including diabetic and arterial/venous insufficiency injuries have become a major burden for healthcare systems worldwide. Demographic changes suggest that wound care will play an even bigger role in the coming decades. Predicting and monitoring response to therapy in wound care is currently largely based on visual inspection with little information on the underlying tissue. Thus, there is an urgent unmet need for innovative approaches that facilitate personalized diagnostics and treatments at the point-of-care. It has been recently shown that ultrasound imaging can monitor response to therapy in wound care, but this work required onerous manual image annotations. In this study we present initial results of a deep learning-based automatic segmentation of cross-sectional wound size in ultrasound images and identify requirements and challenges for future research on this application. Evaluation of the segmentation results underscores the potential of the proposed deep learning approach to complement non-invasive imaging with Dice scores of 0.34 (U-Net, FCN) and 0.27 (ResNet-U-Net) but also highlights the need for improving robustness further. We conclude that deep learning-supported analysis of non-invasive ultrasound images is a promising area of research to automatically extract cross-sectional wound size and depth information with potential value in monitoring response to therapy. © 2022, CC BY-NC-SA.

关键词： Ultrasonic imaging

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Food Photo enhancer of one sample generative adversarial network 1

Food Photo enhancer of one sample generative adversarial net...

引用

1st ACM International Conference on Multimedia in Asia, MMAsia 2019

作者： Wang, Shudan Sun, Liang Dong, Weiming Zhang, Yong Institute of Artificial Intelligence School of Automation and Electrical Engineering University of Science and Technology Beijing China National Laboratory of Pattern Recognition China Tencent Ai Lab China

ISBN: (纸本)9781450368414

Image enhancement is an important branch in the field of image processing. A few existing methods leverage Generative Adversarial Networks (GANs) for this task. However, they have several defects when applied to a specific type of images, such as food photo. First, a large set of original-enhanced image pairs are required to train GANs that have millions of parameters. Such image pairs are expensive to acquire. Second, color distribution of enhanced images generated by previous methods is not consistent with the original ones, which is not expected. To alleviate the issues above, we propose a novel method for food photo enhancement. No original-enhanced image pairs are required except only original images. We investigate Food Faithful Color Semantic Rules in Enhanced Dataset Photo Enhancement (Faith-EDPE) and also carefully design a light generator which can preserve semantic relations among colors. We evaluate the proposed method on public benchmark databases to demonstrate the effectiveness of the proposed method through visual results and user studies. © 2019 Association for Computing Machinery.

关键词： Generative adversarial networks

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Deep Neural Networks based Meta-Learning for Network Intrusion Detection

arXiv

引用

arXiv 2023年

作者： Sohail, Anabia Ayisha, Bibi Hammed, Irfan Zafar, Muhammad Mohsin Alquhayz, Hani Khan, Asifullah Pattern Recognition Lab Department of Computer & Information Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Department of Computer Science Faculty of Computing and Artificial Intelligence Air University E-9 Islamabad Pakistan Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi District Swabi Khyber Pakhtunkhwa23640 Pakistan Department of Computer Science and Information College of Science in Zulfi Majmaah University Al-Majmaah11952 Saudi Arabia Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan

The digitization of different components of industry and inter-connectivity among indigenous networks have increased the risk of network attacks. Designing an intrusion detection system to ensure security of the industrial ecosystem is difficult as network traffic encompasses various attack types, including new and evolving ones with minor changes. The data used to construct a predictive model for computer networks has a skewed class distribution and limited representation of attack types, which differ from real network traffic. These limitations result in dataset shift, negatively impacting the machine learning models' predictive abilities and reducing the detection rate against novel attacks. To address the challenges, we propose a novel deep neural network based Meta-Learning framework;INformation FUsion and Stacking Ensemble (INFUSE) for network intrusion detection. First, a hybrid feature space is created by integrating decision and feature spaces. Five different classifiers are utilized to generate a pool of decision spaces. The feature space is then enriched through a deep sparse autoencoder that learns the semantic relationships between attacks. Finally, the deep Meta-Learner acts as an ensemble combiner to analyze the hybrid feature space and make a final decision. Our evaluation on stringent benchmark datasets and comparison to existing techniques showed the effectiveness of INFUSE with an F-Score of 0.91, Accuracy of 91.6%, and Recall of 0.94 on the Test+ dataset, and an F-Score of 0.91, Accuracy of 85.6%, and Recall of 0.87 on the stringent Test-21 dataset. These promising results indicate the strong generalization capability and the potential to detect network attacks. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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PIPAL: A Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration 16th

PIPAL: A Large-Scale Image Quality Assessment Dataset for Pe...

引用

16th European Conference on Computer Vision, ECCV 2020

作者： Jinjin, Gu Haoming, Cai Haoyu, Chen Xiaoxing, Ye Ren, Jimmy S. Chao, Dong The School of Data Science The Chinese University of Hong Kong Shenzhen China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China SenseTime Research Science Park Hong Kong SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9783030586201

Image quality assessment (IQA) is the key factor for the fast development of image restoration (IR) algorithms. The most recent IR methods based on Generative Adversarial Networks (GANs) have achieved significant improvement in visual performance, but also presented great challenges for quantitative evaluation. Notably, we observe an increasing inconsistency between perceptual quality and the evaluation results. Then we raise two questions: (1) Can existing IQA methods objectively evaluate recent IR algorithms? (2) When focus on beating current benchmarks, are we getting better IR algorithms? To answer these questions and promote the development of IQA methods, we contribute a large-scale IQA dataset, called Perceptual Image Processing Algorithms (PIPAL) dataset. Especially, this dataset includes the results of GAN-based methods, which are missing in previous datasets. We collect more than 1.13 million human judgments to assign subjective scores for PIPAL images using the more reliable "Elo system". Based on PIPAL, we present new benchmarks for both IQA and super-resolution methods. Our results indicate that existing IQA methods cannot fairly evaluate GAN-based IR algorithms. While using appropriate evaluation methods is important, IQA methods should also be updated along with the development of IR algorithms. At last, we improve the performance of IQA networks on GAN-based distortions by introducing anti-aliasing pooling. Experiments show the effectiveness of the proposed method. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark

arXiv

引用

arXiv 2021年

作者： Sun, Hao Xu, Guangxuan Deng, Jiawen Cheng, Jiale Zheng, Chujie Zhou, Hao Peng, Nanyun Zhu, Xiaoyan Huang, Minlie The CoAI Group DCST Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China University of California Los Angeles United States Pattern Recognition Center WeChat AI Tencent Inc China

Dialogue safety problems severely limit the real-world deployment of neural conversational models and have attracted great research interests recently. However, dialogue safety problems remain under-defined and the corresponding dataset is scarce. We propose a taxonomy for dialogue safety specifically designed to capture unsafe behaviors in human-bot dialogue settings, with focuses on context-sensitive unsafety, which is under-explored in prior works. To spur research in this direction, we compile DIASAFETY, a dataset with rich context-sensitive unsafe examples. Experiments show that existing safety guarding tools fail severely on our dataset. As a remedy, we train a dialogue safety classifier to provide a strong baseline for context-sensitive dialogue unsafety detection. With our classifier, we perform safety evaluations on popular conversational models and show that existing dialogue systems still exhibit concerning context-sensitive safety problems. Copyright © 2021, The Authors. All rights reserved.

关键词： Taxonomies

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