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Explainability for Medical Image Captioning 11

Explainability for Medical Image Captioning

引用

11th International Conference on Image Processing Theory, Tools and Applications (IPTA)

作者： Beddiar, Djamila Oussalah, Mourad Tapio, Seppanen Univ Oulu CMVS Oulu Finland MIPT Fac Med Oulu Finland

ISBN: (纸本)9781665469647

Medical image captioning is the process of generating clinically significant descriptions to medical images, which has many applications among which medical report generation is the most frequent one. In general, automatic captioning of medical images is of great interest for medical experts since it offers assistance in diagnosis, disease treatment and automating the workflow of the health practitioners. Recently, many efforts have been put forward to obtain accurate descriptions but medical image captioning still provides weak and incorrect descriptions. To alleviate this issue, it is important to explain why the model produced a particular caption based on some specific features. This is performed through Artificial Intelligence Explainability (XAI), which aims to unfold the 'black-box' feature of deeplearning based models. We present in this paper an explainable module for medical image captioning that provides a sound interpretation of our attention-based encoder-decoder model by explaining the correspondence between visual features and semantic features. We exploit for that, self-attention to compute word importance of semantic features and visual attention to compute relevant regions of the image that correspond to each generated word of the caption in addition to visualization of visual features extracted at each layer of the Convolutional Neural Network (CNN) encoder. We finally evaluate our model using the ImageCLEF medical captioning dataset.

关键词： Image Captioning Medical images encoder-decoder Attention-maps Artificial Intelligence Explainability

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RBBA: ResNet - BERT - Bahdanau Attention for Image Caption Generator 14

RBBA: ResNet - BERT - Bahdanau Attention for Image Caption G...

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Hoang, Duc-Hieu Tran, Anh-Khoa Minh Dang, Duc Ngoc Tran, Phuong-Nam Dang-Ngoc, Hanh Nguyen, Cuong Tuan Ton Duc Thang University Faculty of Electrical & Electronics Eng. Ho Chi Minh City Viet Nam Ton Duc Thang University Faculty of Electrical and Electronics Ho Chi Minh City Viet Nam Fpt University Dept. of Computing Fundamental Ho Chi Minh City Viet Nam University of Technology Sydney School of Electrical and Data Engineering SydneyNSW Australia Fpt University Dept. of Information Technology Specialization Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350313277

In recent years, the topic of image caption generators has gained significant attention. Several successful projects have emerged in this field, showcasing notable advancements. Image caption generators automatically generate descriptive captions for images through the encoder and decoder mechanisms. The encoder leverages computer vision models, while the decoder utilizes natural language processing models. In this study, we aim to assess a comprehensive set of seven distinct methodologies, including six existing methods from prior research and one newly proposed. These methods are trained and evaluated with bilingual evaluation (BLEU) on the Flickr8K dataset. In our experiments, the proposed ResNet50 - BERT - Bahdanau Attention model outperforms the other models in terms of the BLEU-1 score of 0.532143 and BLEU-4 score of 0.126316. © 2023 IEEE.

关键词： BLEU Deep learning encoder-decoder Flickr8K Image Caption Natural language processing

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SMALL BOWEL MUCOSA SEGMENTATION FOR FRAME CHARACTERIZATION IN VIDEOS OF ENDOSCOPIC CAPSULES 16

SMALL BOWEL MUCOSA SEGMENTATION FOR FRAME CHARACTERIZATION I...

引用

16th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Pinheiro, Gil Coelho, Paulo Mourao, Mariana Salgado, Maria Oliveira, Helder P. Cunha, Antonio Inst Engn Sistemas & Comp Tecnol & Ciencia Porto Portugal Inst Politecn Leiria Leiria Portugal Univ Tras Os Montes & Alto Douro Vila Real Portugal Ctr Hosp Porto Porto Portugal Univ Porto Fac Ciencias Porto Portugal

ISBN: (纸本)9781538636411

Endoscopic capsules are vitamin-sized devices that leverage from a small wireless camera to create 8 to 10 hour videos of the patients' entire digestive tract, still being the leading tool to diagnose small bowel diseases. The revision of the produced videos is a very time-consuming task, currently conducted manually and frame-by-frame by an expert. Since endoscopic videos usually contain a considerable amount of frames where the mucosa is not clearly visible, the segmentation of the informative regions is a vital component to reduce the necessary time to review each exam. In this work, a CNN encoder-decoder architecture is applied to segment informative regions in small bowel frames of videos of endoscopic capsules. The network was trained and tested with a dataset of 2,929 manually annotated images, achieving a 91.2% Dice coefficient and 83.9% IoU. Furthermore, a video-wise analysis based on the amount of informative pixels in each frame is done.

关键词： Endoscopic capsule mucosa segmentation informative frames encoder-decoder deep learning

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Single Image Dehazing using CNN 7

Single Image Dehazing using CNN

引用

7th International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)

作者： Rashid, Huzaifa Zafar, Nauman Iqbal, M. Javed Dawood, Hassan Dawood, Hussain Univ Engn & Technol Dept Comp Sci Taxila Pakistan Univ Engn & Technol Dept Software Engn Taxila Pakistan Univ Jeddah Fac Comp & Informat Technol Jeddah Saudi Arabia

Haze is a natural phenomenon in which the dust, smoke and other particles alter the vision of the sky to reduce the visibility. Hazy images cause various visibility problems for traffic user, tourists everywhere, especially in hilly areas where haze and fog are very common. In this paper, a method for single image dehazing using convolutional neural network is proposed. Outdoor images have been used on which particular filters are applied to fmd the haze in image. Hazy images contain small value in only one-color alpha channel from Red, Blue, green RGB channel. The intensity of these pixels is mainly bestowed by air light depth map. Estimating these low value points of haze transmission map are useful to obtain a high quality dehazed image. An end-to-end encoder-decoder training model is utilized to achieve a high quality dehazed image. The approach is validated on datasets which consists of around 1500 outdoor images. The method also gives transmission map of the hazy image which can further be used to enhance visibility of the scene. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Image Dehazing Guided filter Transmission map Depth map Atmospheric light encoder-decoder

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MULTILINGUAL SPEECH RECOGNITION WITH A SINGLE END-TO-END MODEL

MULTILINGUAL SPEECH RECOGNITION WITH A SINGLE END-TO-END MOD...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Toshniwal, Shubham Sainath, Tara N. Weiss, Ron J. Li, Bo Moreno, Pedro Weinstein, Eugene Rao, Kanishka Toyota Technol Inst Chicago IL 60637 USA Google Inc Mountain View CA USA

ISBN: (纸本)9781538646588

Training a conventional automatic speech recognition (ASR) system to support multiple languages is challenging because the sub-word unit, lexicon and word inventories are typically language specific. In contrast, sequence-to-sequence models are well suited for multilingual ASR because they encapsulate an acoustic, pronunciation and language model jointly in a single network. In this work we present a single sequence-to-sequence ASR model trained on 9 different Indian languages, which have very little overlap in their scripts. Specifically, we take a union of language-specific grapheme sets and train a grapheme-based sequence-to-sequence model jointly on data from all languages. We find that this model, which is not explicitly given any information about language identity, improves recognition performance by 21% relative compared to analogous sequence-to-sequence models trained on each language individually. By modifying the model to accept a language identifier as an additional input feature, we further improve performance by an additional 7% relative and eliminate confusion between different languages.

关键词： ASR speech recognition multilingual encoder-decoder seq2seq Indian

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Improved Recurrent Neural Networks (RNN) based Intelligent Fund Transaction Model

Improved Recurrent Neural Networks (RNN) based Intelligent F...

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IEEE Global Communications Conference (IEEE GLOBECOM)

作者： Hu, Gang Ye, Yi Zhang, Yin Hossain, M. Shantim Zhongnan Univ Econ & Law Sch Informat & Safety Engn Wuhan Peoples R China King Saud Univ Comp & Informat Sci Riyadh Saudi Arabia

ISBN: (纸本)9781728109602

Fund correlation analysis can guide investors' investment and wealth management, avoiding the selection of highly relevant funds in the investment process, which can make the risk sharing among funds. There is a strong dependence between the features of the fund data and a long-term dependence between the output of different time steps, which makes it difficult to obtain good performance in the fund data in the data analysis model used in the traditional intelligent investment system. This has brought difficulties to fund correlation analysis. In order to solve the above problems, this paper uses an encoder-decoder model combined with the attention mechanism-Improved RNN model. The encoder-decoder model has made great strides in the application of financial time series analysis. And the attention mechanism can select specific feature inputs and previous time step outputs, both of which are highly correlated with the current output, making system predictions more efficient. This paper applies this model to the historical data set containing multiple public funds. The results show that the fund intelligent investment system proposed in this paper performs hest.

关键词： Fund intelligent transaction model Deep learning encoder-decoder Attention mechanism

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Context-aware Variational Trajectory Encoding and Human Mobility Inference 19

Context-aware Variational Trajectory Encoding and Human Mobi...

引用

World Wide Web Conference (WWW)

作者： Zhou, Fan Yue, Xiaoli Trajcevski, Goce Zhong, Ting Zhang, Kunpeng Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China Iowa State Univ Ames IA USA Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781450366748

Unveiling human mobility patterns is an important task for many downstream applications like point-of-interest (POI) recommendation and personalized trip planning. Compelling results exist in various sequential modeling methods and representation techniques. However, discovering and exploiting the context of trajectories in terms of abstract topics associated with the motion can provide a more comprehensive understanding of the dynamics of patterns. We propose a new paradigm for moving pattern mining based on learning trajectory context, and a method - Context-Aware Variational Trajectory Encoding and Human Mobility Inference (CATHI) - for learning user trajectory representation via a framework consisting of: (1) a variational encoder and a recurrent encoder;(2) a variational attention layer;(3) two decoders. We simultaneously tackle two subtasks: (T1) recovering user routes (trajectory reconstruction);and (T2) predicting the trip that the user would travel (trajectory prediction). We show that the encoded contextual trajectory vectors efficiently characterize the hierarchical mobility semantics, from which one can decode the implicit meanings of trajectories. We evaluate our method on several public datasets and demonstrate that the proposed CATHI can efficiently improve the performance of both subtasks, compared to state-of-the-art approaches.

关键词： human mobility variational inference encoder-decoder

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A Spatio-Temporal Graph Transformer Network for Multi-Pedestrain Trajectory Prediction 20

A Spatio-Temporal Graph Transformer Network for Multi-Pedest...

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IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)

作者： Zhu, Jingfei Lian, Zhichao Jiang, Zhukai Nanjing Univ Sci & Technol Dept Comp Sci & Engn Nanjing Peoples R China Nanjing Univ Sci & Technol Dept Cyberspace Secur Nanjing Peoples R China

ISBN: (数字)9781665462976

ISBN: (纸本)9781665462976

Trajectory prediction is widely used in many fields, e.g., in autonomous driving system and traffic surveillance system, good trajectory prediction algorithm can help prevent collisions or traffic jam. In practical scenarios, pedestrian trajectory is mainly affected by two factors: 1) Temporality. pedestrian's movement is continuous, and there is a certain connection between the future and the history of the trajectory. 2) Spatiality. In the scenario of multiple pedestrians, one can be influenced by the people around and may be forced to change their movement path. In this paper, we propose a spatio-temporal graph transformer network and make the following contributions :1) A new decoder structure is proposed. 2) A new memory mechanism is used to improve the continuity of the temporality of the trajectory. 3) HuberLoss is used as the loss function of the network for the first time and shows good results. We demonstrate that our model effectively improves the accuracy of prediction by validation on five common datasets of pedestrian trajectory in ETH[9] and UCY[10].

关键词： transformer encoder-decoder memory graph

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Using Quadcopter for Vision-based Tracking of High-Rise Vertical Structure 20

Using Quadcopter for Vision-based Tracking of High-Rise Vert...

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20th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)

作者： Gupta, Ayush Shukla, Amit Sivarathri, Ashok Kumar Pandya, Naisarg Indian Inst Technol Mandi Sch Mech & Mat Engn Mandi Himachal Prades India Indian Inst Technol Mandi Ctr Artificial Intelligence & Robot Mandi Himachal Prades India

ISBN: (纸本)9798331516246;9798331516239

This research paper focuses on developing vision-based autonomous navigation and motion control algorithms for quadcopters to track high-rise industrial vertical structures. A camera, fixed on the drone will capture the Red-Green-Blue (RGB) channel image and will feed to the image processing node, which is a convolution neural network (CNN) based encoder-decoder model for image segmentation. The segmented image will go to the classical computer vision algorithm to get the desired features for navigation and the motion control algorithm. In this paper, we have implemented a nonlinear sliding mode control algorithm for motion control and discuss the results of positioning and motion control of the drone for the motion in circular and spiral paths. The entire experimentation is performed on the DJI M100 drone with the Nvidia Jetson Xavier NX as an onboard computer.

关键词： Autonomous tracking Computer Vision encoder-decoder and sliding mode control

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Image Steganography Scheme Using Dilated Convolutional Network 12

Image Steganography Scheme Using Dilated Convolutional Netwo...

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12th International Conference on Information and Communication Systems (ICICS)

作者： Kich, Ismail Ameur, El Bachir Taouil, Youssef Benhfid, Amine Ibn Tofail Univ Fac Sci Comp Sci Res Lab Kenitra Morocco

ISBN: (纸本)9781665433518

Nowadays, Convolutional Neural Networks (CNN) have allowed us to solve many problems, difficult to solve with classical methods, in different fields of applications. The use of this technique in the field of modern steganography has improved the performance of steganographic schemes in terms of concealability and invisibility. In this article, we propose a system based on CNN in order to hide a color image in another color image of the same size. The proposed system is based on Auto-encoder network and U-net architecture. The network is subdivided into two sub-networks, the first is for the concealment of the image secret by the sender, the second is for its extraction by the receiver. The network is end to end trained to ensure the integrity of the concealment and extraction process. The tests were performed on challenging images dataset publicly available, such as ImageNet, LFW, PASCAL-VOC12. The results show that the proposed steganographic scheme can hide a color image in another one of the same sizes, i.e. a capacity of 24 bpp, with acceptable PSNR and SSIM values compared to other previous work.

关键词： Information security Image steganography Deep Neural Network CNN encoder-decoder

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