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OXnet: Deep omni-supervised thoracic disease detection from chest X-rays

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arXiv 2021年

作者： Luo, Luyang Chen, Hao Zhou, Yanning Lin, Huangjing Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Imsight AI Research Lab Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

Chest X-ray (CXR) is the most typical diagnostic X-ray examination for screening various thoracic diseases. Automatically localizing lesions from CXR is promising for alleviating radiologists' reading burden. However, CXR datasets are often with massive image-level annotations and scarce lesion-level annotations, and more often, without annotations. Thus far, unifying different supervision granularities to develop thoracic disease detection algorithms has not been comprehensively addressed. In this paper, we present OXnet, the first deep omni-supervised thoracic disease detection network to our best knowledge that uses as much available supervision as possible for CXR diagnosis. We first introduce supervised learning via a one-stage detection model. Then, we inject a global classification head to the detection model and propose dual attention alignment to guide the global gradient to the local detection branch, which enables learning lesion detection from image-level annotations. We also impose intra-class compactness and inter-class separability with global prototype alignment to further enhance the global information learning. Moreover, we leverage a soft focal loss to distill the soft pseudo-labels of unlabeled data generated by a teacher model. Extensive experiments on a large-scale chest X-ray dataset show the proposed OXnet outperforms competitive methods with significant margins. Further, we investigate omni-supervision under various annotation granularities and corroborate OXnet is a promising choice to mitigate the plight of annotation shortage for medical image diagnosis.2 © 2021, CC BY-NC-ND.

关键词： Deep learning

NTIRE 2023 Challenge on 360° Omnidirectional Image and Video Super-Resolution: Datasets, Methods and Results

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NTIRE 2023 Challenge on 360° Omnidirectional Image and Vide...

2023 IEEE/CVF Conference on computer vision and Pattern Recognition Workshops, CVPRW 2023

作者： Cao, Mingdeng Mou, Chong Yu, Fanghua Wang, Xintao Zheng, Yinqiang Zhang, Jian Dong, Chao Li, Gen Shan, Ying Timofte, Radu Sun, Xiaopeng Li, Weiqi Sheng, Xuhan Chen, Bin Ma, Haoyu Cheng, Ming Zhao, Shijie Huang, Huaibo Zhou, Xiaoqiang Ai, Yuang He, Ran Wu, Renlong Yang, Yi Zhang, Zhilu Zhang, Shuohao Li, Junyi Chen, Yunjin Ren, Dongwei Zuo, Wangmeng Yang, Hao-Hsiang Chen, Yi-Chung Huang, Zhi-Kai Chen, Wei-Ting Chiang, Yuan-Chun Chang, Hua-En Chen, I.-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Zhang, Zebin Zhang, Jiaqi Wang, Yuhui Cui, Shuhao Huang, Junshi Zhu, Li Tian, Shuman Yu, Wei Luo, Bingchun Cui, Wanwan Xu, Tianyu Li, Chunyang Bao, Long Sun, Heng Zhang, Zhenyu Wang, Qian The University of Tokyo Japan Arc Lab Tencent Pcg China Peking University China Shenzhen Institute of Advanced Technology Cas China Platform Technologies Tencent Online Video China Computer Vision Lab Ifi & Caidas University of Würzburg Germany ByteDance China Peking University Shenzhen Graduate School China Mais&cripac Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China University of Science and Technology of China China Beijing Institute of Technology China School of Information Science and Technology ShanghaiTech University China Harbin Institute of Technology China Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States ShanghaiTech University China Meituan China Xiaomi Inc

ISBN: (纸本)9798350302493

This report introduces two high-quality datasets Flickr360 and ODV360 for omnidirectional image and video super-resolution, respectively, and reports the NTIRE 2023 challenge on 360° omnidirectional image and video super-resolution. Unlike ordinary 2D images/videos with a narrow field of view, omnidirectional images/videos can represent the whole scene from all directions in one shot. There exists a large gap between omnidirectional image/video and ordinary 2D image/video in both the degradation and restoration processes. The challenge is held to facilitate the development of omnidirectional image/video super-resolution by considering their special characteristics. In this challenge, two tracks are provided: one is the omnidirectional image super-resolution and the other is the omnidirectional video super-resolution. The task of the challenge is to super-resolve an input omnidirectional image/video with a magnification factor of ×4. Realistic omnidirectional downsampling is applied to construct the datasets. Some general degradation(e.g., video compression) is also considered for the video track. The challenge has 100 and 56 registered participants for those two tracks. In the final testing stage, 7 and 3 participating teams submitted their results, source codes, and fact sheets. Almost all teams achieved better performance than baseline models by integrating omnidirectional characteristics, reaching compelling performance on our newly collected Flickr360 and ODV360 datasets. © 2023 IEEE.

关键词： Image compression

Reinforcement learning based path exploration for sequential explainable recommendation

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arXiv 2021年

作者： Li, Yicong Chen, Hongxu Li, Yile Li, Lin Yu, Philip S. Xu, Guandong Data Science and Machine Intelligence Lab Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia School of Transportation Engineering Tongji University Shanghai200092 China College of Computer Science and Technology Wuhan University of Technology Wuhan430070 China Department of Computer Science University of Illinois at Chicago ChicagoIL60637 United States

Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information provided by knowledge graphs. Most existing explainable recommendations only utilize static knowledge graphs and ignore the dynamic user-item evolutions, leading to less convincing and inaccurate explanations. Although there are some works that realize that modelling user's temporal sequential behaviour could boost the performance and explainability of the recommender systems, most of them either only focus on modelling user's sequential interactions within a path or independently and separately of the recommendation mechanism. In this paper, we propose a novel Temporal Meta-path Guided Explainable Recommendation leveraging Reinforcement Learning (TMER-RL), which utilizes reinforcement item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendation. Compared with existing works that use heavy recurrent neural networks to model temporal information, we propose simple but effective neural networks to capture users' historical item features and path-based context to characterize the next purchased item. Extensive evaluations of TMER on two real-world datasets show state-of-the-art performance compared against recent strong baselines. © 2021, CC BY.

关键词： Reinforcement learning

Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks

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arXiv 2021年

作者： Ma, Chen Guo, Xiangyu Chen, Li Yong, Jun-Hai Wang, Yisen School of Software BNRist Tsinghua University Beijing China Department of Computer Science and Engineering University at Buffalo BuffaloNY United States Key Lab. of Machine Perception School of Artificial Intelligence Peking University Beijing China Institute for Artificial Intelligence Peking University Beijing China

One major problem in black-box adversarial attacks is the high query complexity in the hard-label attack setting, where only the top-1 predicted label is available. In this paper, we propose a novel geometric-based approach called Tangent Attack (TA), which identifies an optimal tangent point of a virtual hemisphere located on the decision boundary to reduce the distortion of the attack. Assuming the decision boundary is locally flat, we theoretically prove that the minimum `2 distortion can be obtained by reaching the decision boundary along the tangent line passing through such tangent point in each iteration. To improve the robustness of our method, we further propose a generalized method which replaces the hemisphere with a semi-ellipsoid to adapt to curved decision boundaries. Our approach is free of pre-training. Extensive experiments conducted on the ImageNet and CIFAR-10 datasets demonstrate that our approach can consume only a small number of queries to achieve the low-magnitude distortion. The implementation source code is released online at https://***/machanic/TangentAttack. Copyright © 2021, The Authors. All rights reserved.

关键词： Iterative methods

Cardiovascular disease identification using a hybrid CNN-LSTM model with explainable AI

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Informatics in Medicine Unlocked 2023年 42卷

作者： Hossain, Md Maruf Ali, Md Shahin Ahmed, Md Mahfuz Rakib, Md Rakibul Hasan Kona, Moutushi Akter Afrin, Sadia Islam, Md Khairul Ahsan, Md Manjurul Raj, Sheikh Md Razibul Hasan Rahman, Md Habibur Department of Biomedical Engineering Islamic University Kushtia 7003 Bangladesh Department of Food and Nutrition Government College of Applied Human Science Azimpur Dhaka 1205 Bangladesh Department of Radiology Northwestern University Machine and Hybrid Intelligence Lab Chicago 60611 IL United States Industrial & Production Engineering Shahjalal University of Science and Technology Sylhet 3114 Bangladesh Department of Information and Communication Technology Islamic University Kushtia 7003 Bangladesh Department of Computer Science and Engineering Islamic University Kushtia 7003 Bangladesh Center for Advanced Bioinformatics and Artificial Intelligent Research Islamic University Kushtia 7003 Bangladesh

Cardiovascular disease (CVD) is a leading cause of death worldwide, with millions dying each year. The identification and early diagnosis of CVD are critical in preventing adverse health outcomes. Hence, this study proposes a hybrid deep learning (DL) model that combines a convolutional neural network (CNN) and long short-term memory (LSTM) to identify CVD from the clinical data. This study utilizes CNN to extract the relevant features from the input data and the LSTM network to process sequential data and capture dependencies and patterns over time. This study provides insights into the potential of a hybrid DL model combined with feature engineering and explainable AI to improve the accuracy and interpretability of CVD prediction. We evaluated our model on a publicly available dataset where the proposed CNN-LSTM achieved a high accuracy of 73.52% and 74.15% with and without feature engineering, respectively, in identifying individuals with CVD, which is the best result compared to the current state-of-the-art model. The results of this study demonstrate the potential of DL models for the early diagnosis of CVD. Our proposed CNN-LSTM model also incorporates explainable AI to identify the top features responsible for CVD. They could be used to develop more eﬀective screening tools in clinical practice. © 2023 The Authors

关键词： Cardiovascular disease CNN-LSTM Deep learning Explainable AI Feature engineering

Recent advancements and challenges of NLP-based sentiment analysis: A state-of-the-art review

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Natural Language Processing Journal

Natural Language Processing Journal 2024年 6卷

作者： Jamin Rahman Jim Md Apon Riaz Talukder Partha Malakar Md Mohsin Kabir Kamruddin Nur M.F. Mridha Advanced Machine Intelligence Research Lab Banani Dhaka 1213 Dhaka Bangladesh Department of Computer Science American International University-Bangladesh Dhaka 1229 Bangladesh Faculty of Informatics Eötvös Loránd University H-1117 Budapest Hungary

Sentiment analysis is a method within natural language processing that evaluates and identifies the emotional tone or mood conveyed in textual data. Scrutinizing words and phrases categorizes them into positive, negative, or neutral sentiments. The significance of sentiment analysis lies in its capacity to derive valuable insights from extensive textual data, empowering businesses to grasp customer sentiments, make informed choices, and enhance their offerings. For the further advancement of sentiment analysis, gaining a deep understanding of its algorithms, applications, current performance, and challenges is imperative. Therefore, in this extensive survey, we began exploring the vast array of application domains for sentiment analysis, scrutinizing them within the context of existing research. We then delved into prevalent pre-processing techniques, datasets, and evaluation metrics to enhance comprehension. We also explored machine Learning, Deep Learning, Large Language Models and Pre-trained models in sentiment analysis, providing insights into their advantages and drawbacks. Subsequently, we precisely reviewed the experimental results and limitations of recent state-of-the-art articles. Finally, we discussed the diverse challenges encountered in sentiment analysis and proposed future research directions to mitigate these concerns. This extensive review provides a complete understanding of sentiment analysis, covering its models, application domains, results analysis, challenges, and research directions.

关键词： Sentiment classification Text classification Natural language processing Emotion detection Sentiment analysis

View-consistent metal segmentation in the projection domain for metal artifact reduction in CBCT - An investigation of potential improvement

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arXiv 2021年

作者： Gottschalk, Tristan Matthias Maier, Andreas Kordon, Florian Kreher, Björn W. Pattern Recognition Lab Department of Computer Science Friedrich-Alexander University Erlangen-Nuremberg Erlangen91058 Germany Friedrich-Alexander University Erlangen-Nuremberg Erlangen91052 Germany Siemens Healthcare GmbH Forchheim91301 Germany Machine Intelligence Department of Computer Science Friedrich-Alexander University Erlangen-Nuremberg Erlangen91058 Germany Siemens Healthcare GmbH Siemensstraße 1 Forchheim91301 Germany

The positive outcome of a trauma intervention depends on an intraoperative evaluation of inserted metallic implants. Due to occurring metal artifacts, the quality of this evaluation heavily depends on the performance of so-called Metal Artifact Reduction methods (MAR). The majority of these MAR methods require prior segmentation of the inserted metal objects. Therefore, typically a rather simple thresholding-based segmentation method in the reconstructed 3D volume is applied, despite some major disadvantages. With this publication, the potential of shifting the segmentation task to a learning-based, view-consistent 2D projection-based method on the downstream MAR's outcome is investigated. For segmenting the present metal, a rather simple learning-based 2D projection-wise segmentation network that is trained using real data acquired during cadaver studies, is examined. To overcome the disadvantages that come along with a 2D projection-wise segmentation, a Consistency Filter is proposed. The influence of the shifted segmentation domain is investigated by comparing the results of the standard fsMAR with a modified fsMAR version using the new segmentation masks. With a quantitative and qualitative evaluation on real cadaver data, the investigated approach showed an increased MAR performance and a high insensitivity against metal artifacts. For cases with metal outside the reconstruction's FoV or cases with vanishing metal, a significant reduction in artifacts could be shown. Thus, increases of up to roughly 3 dB w.r.t. the mean PSNR metric over all slices and up to 9 dB for single slices were achieved. The shown results reveal a beneficial influence of the shift to a 2D-based segmentation method on real data for downstream use with a MAR method, like the fsMAR. The nature of the method further suggests the same beneficial behavior for all (also recent data-driven) MAR methods, that for now comprise a 3D-volume-based segmentation step for subsequent inpainting. Copyrigh

关键词： Metals

Corrections to “RespiroDynamics: A Multifaceted Dataset for Enhanced Lung Health Assessment Using Deep Learning”

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IEEE Access 2024年 12卷 118831-118831页

作者： Ahmed Sharshar Muhammad Sharshar Hosam Elhady Ahmed Aboeitta Youssef Nafea Yasser Ashraf Mohammad Yaqub Mohsen Guizani Department of Computer Vision Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi United Arab Emirates Cyber-Physical Laboratory Egypt-Japan University of Science and Technology (E-JUST) Alexandria Egypt Department of Natural Language Processing MBZUAI Abu Dhabi United Arab Emirates Department of Machine Learning MBZUAI Abu Dhabi United Arab Emirates

Presents corrections to the paper, RespiroDynamics: A Multifaceted Dataset for Enhanced Lung Health Assessment Using Deep Learning.

关键词： Deep learning Lung

ScanMix: Learning from Severe label Noise via Semantic Clustering and Semi-Supervised Learning

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arXiv 2021年

作者： Sachdeva, Ragav Cordeiro, Filipe Rolim Belagiannis, Vasileios Reid, Ian Carneiro, Gustavo Visual Geometry Group Department of Engineering Science University of Oxford United Kingdom School of Computer Science Australian Institute for Machine Learning Australia Visual Computing Lab Department of Computing Universidade Federal Rural de Pernambuco Brazil Otto-von-Guericke-Universität Magdeburg Germany Centre for Vision Speech and Signal Processing University of Surrey United Kingdom

We propose a new training algorithm, ScanMix, that explores semantic clustering and semi-supervised learning (SSL) to allow superior robustness to severe label noise and competitive robustness to non-severe label noise problems, in comparison to the state of the art (SOTA) methods. ScanMix is based on the expectation maximisation framework, where the E-step estimates the latent variable to cluster the training images based on their appearance and classification results, and the M-step optimises the SSL classification and learns effective feature representations via semantic clustering. We present a theoretical result that shows the correctness and convergence of ScanMix, and an empirical result that shows that ScanMix has SOTA results on CIFAR-10/-100 (with symmetric, asymmetric and semantic label noise), Red Mini-ImageNet (from the Controlled Noisy Web labels), Clothing1M and Webvision. In all benchmarks with severe label noise, our results are competitive to the current SOTA. © 2021, CC BY.

关键词： Semantics