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7th International Conference on Computer Vision and image processing, CVIP 2022

作者： Innani, Shubham Dutande, Prasad Baheti, Bhakti Baid, Ujjwal Talbar, Sanjay Center of Excellence Signal and Image Processing Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India

ISBN: (纸本)9783031314063

Patients diagnosed with skin cancer like melanoma are prone to a high mortality rate. Automatic lesion analysis is critical in skin can¬cer diagnosis and ensures effective treatment. The computer-aided diag¬nosis of such skin cancer in dermoscopic images can significantly reduce the clinicians’ workload and help improve diagnostic accuracy. Although researchers are working extensively to address this problem, early detec¬tion and accurate identification of skin lesions remain challenging. This research focuses on a two-step framework for skin lesion segmentation followed by classification for lesion analysis. We explored the effective¬ness of deep convolutional neural network (CNN) based architectures by designing an encoder-decoder architecture for skin lesion segmenta¬tion and CNN based classification network. The proposed approaches are evaluated quantitatively in terms of the Accuracy, mean Intersection over Union(mIoU) and Dice Similarity Coefficient. Our cascaded end-to-end deep learning-based approach is the first of its kind, where the classification accuracy of the lesion is significantly improved because of prior segmentation. The code is available at https://***/shubhaminnani/skin/lesion. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： image enhancement

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Modified ResNet for Volumetric Segmentation of Liver and Its Tumor from 3D CT

Modified ResNet for Volumetric Segmentation of Liver and Its...

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International Conference on Artificial Intelligence and Sustainable engineering, AISE 2020

作者： Mourya, Gajendra Kumar Bhatia, Dinesh Gogoi, Manashjit Talbar, S.N. Baid, Ujjwal Dudante, Prasad Department of Biomedical Engineering North-Eastern Hill University Meghalaya Shillong India Center of Excellence in Signal and Image Processing Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India

ISBN: (纸本)9789811685415

Liver tumor segmentation is the first step to investigate severity of liver disorder. The conventional automatic segmentation methods highly depend on hand-crafted features and priori information, which are tedious and time consuming for medical images. Fully convolutional network (FCN) of the deep learning model can extract features automatically and hierarchically learning concerning layers depth. In this article, we demonstrated the application of modified ResNet for the automatic segmentation of liver and tumor segmentation from 3D CT medical imaging datasets. The FCN ResNet architecture is modified while changing the levels at encoder and decoder ends to overcome the limitation of the conventional segmentation technique. The network input and output image dimension kept the same for training and testing for the designed model. LiTS and 3Dirac data use for training and testing the model. Multifold training datasets strategy utilized. Results were evaluated quantitatively and achieved DICE 0.96 and 0.81 for liver and tumor, respectively. The modified technique will assist to interventional radiologist workflow and faster the diagnostic and therapeutic procedures. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computerized tomography

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Bag-Level Multiple Instance Learning for Acute Stress Detection from Video Data

Bag-Level Multiple Instance Learning for Acute Stress Detect...

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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Brügge, Nele Sophie Korda, Alexandra Borgwardt, Stefan Andreou, Christina Giannakakis, Giorgos Handels, Heinz German Research Center for Artificial Intelligence AI in Medical Image and Signal Processing Lübeck Germany Translational Psychiatry Department of Psychiatry and Psychotherapy University of Luebeck Lübeck23562 Germany Heraklion Greece Department of Electronic Engineering Hellenic Mediterranean University Chania Greece Institute of Agri-food and Life Sciences University Research and Innovation Center Hellenic Mediterranean University Heraklion Greece Institute of Medical Informatics University of Luebeck Lübeck Germany

Stress detection is a complex challenge with implications for health and well-being. It often relies on sensors recording biomarkers and biosignals, which can be uncomfortable and alter behaviour. Video-based facial feature analysis offers a noninvasive alternative. This study explores video-level stress detection using top-k Multiple Instance Learning applied to medical videos. The approach is motivated by the assumption that subjects partly show normal behaviour while performing stressful experimental tasks. Our contributions include a tailored temporal feature network and optimised data utilisation by additionally incorporating bottom-k snippets. Leave-five-subjects-out stress detection results of 95.46% accuracy and 95.49% F1 score demonstrate the potential of our approach, outperforming the baseline methods. Additionally, through multiple instance learning, it is possible to show which temporal video segments the network pays particular attention to. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Adversarial machine learning

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Tiny noise, big mistakes: adversarial perturbations induce errors in brain–computer interface spellers

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National Science Review 2021年第4期8卷 78-90页

作者： Xiao Zhang Dongrui Wu Lieyun Ding Hanbin Luo Chin-Teng Lin Tzyy-Ping Jung Ricardo Chavarriaga Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and Technology School of Civil Engineering and Mechanics Huazhong University of Science and Technology Centre of Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney Swartz Center for Computational Neuroscience Institute for Neural ComputationUniversity of California San Diego Center for Advanced Neurological Engineering Institute of Engineering in Medicine University of California San Diego ZHAW Data Lab Zürich University of Applied Sciences

An electroencephalogram(EEG)-based brain–computer interface(BCI) speller allows a user to input text to a computer by thought. It is particularly useful to severely disabled individuals, e.g. amyotrophic lateral sclerosis patients, who have no other effective means of communication with another person or a *** studies so far focused on making EEG-based BCI spellers faster and more reliable; however, few have considered their security. This study, for the first time, shows that P300 and steady-state visual evoked potential BCI spellers are very vulnerable, i.e. they can be severely attacked by adversarial perturbations,which are too tiny to be noticed when added to EEG signals, but can mislead the spellers to spell anything the attacker wants. The consequence could range from merely user frustration to severe misdiagnosis in clinical applications. We hope our research can attract more attention to the security of EEG-based BCI spellers, and more broadly, EEG-based BCIs, which has received little attention before.

关键词： electroencephalogram brain-computer interfaces BCI spellers adversarial examples

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Generative Adversarial Networks based Skin Lesion Segmentation

arXiv

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arXiv 2023年

作者： Innani, Shubham Dutande, Prasad Baid, Ujjwal Pokuri, Venu Bakas, Spyridon Talbar, Sanjay Baheti, Bhakti Guntuku, Sharath Chandra Center of Excellence in Signal and Image Processing Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia United States Department of Computer and Information Science University of Pennsylvania Philadelphia United States

Skin cancer is a serious condition that requires accurate diagnosis and treatment. One way to assist clinicians in this task is using computer-aided diagnosis (CAD) tools that automatically segment skin lesions from dermoscopic images. We propose a novel adversarial learning-based framework called Efficient-GAN (EGAN) that uses an unsupervised generative network to generate accurate lesion masks. It consists of a generator module with a top-down squeeze excitation-based compound scaled path, an asymmetric lateral connection-based bottom-up path, and a discriminator module that distinguishes between original and synthetic masks. A morphology-based smoothing loss is also implemented to encourage the network to create smooth semantic boundaries of lesions. The framework is evaluated on the International Skin Imaging Collaboration (ISIC) Lesion Dataset 2018. It outperforms the current state-of-the-art skin lesion segmentation approaches with a Dice coefficient, Jaccard similarity, and Accuracy of 90.1%, 83.6%, and 94.5%, respectively. We also design a lightweight segmentation framework (MGAN) that achieves comparable performance as EGAN but with an order of magnitude lower number of training parameters, thus resulting in faster inference times for low compute resource settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Institute for Ai Tsinghua University BNRist Center Thbi Lab Dept. of Comp. Sci. and Tech. China School of Computer Science and Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province China Saic Motor Ai Lab Zhongguancun Laboratory China

ISBN: (纸本)9798350301298

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. © 2023 IEEE.

关键词： Autonomous driving

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Laser Scar Classification in Retinal Fundus images Using Wavelet Transform and Local Variance 1st

Laser Scar Classification in Retinal Fundus Images Using Wav...

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1st International Symposium on Computer Vision and Machine Intelligence in Medical image Analysis, ISCMM 2019 in conjunction with the 2nd International Conference on Advanced Computational and Communication Paradigms, ICACCP 2019

作者： Raut, Rashmi Sapate, Visharad Rokde, Abhay Pachade, Samiksha Porwal, Prasanna Kokare, Manesh Center of Excellence in Signal and Image Processing Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India

ISBN: (纸本)9789811387975

Diabetic retinopathy (DR) affects the vision of the person and may eventually lead to blindness. In the initial stage of the disease, patients are treated with a laser to restrict its progression. Such laser treatment leaves behind scars on the retina and patients are advised to undergo screening regularly to track further complications. This paper presents a novel retinal background characterization approach that explores the potential of discrete wavelet transform and rotational-invariant variance features for texture classification of retinal images with and without laser marks. For this experiment, different classifiers, namely, support vector machine, naive Bayes, neural network, and random forest classifiers are tested. We used two publicly available datasets, namely, LMD-DRS and LMD-BAPT. In all cases, the proposed approach obtained the sensitivity, specificity, and accuracy values higher than 68.9%, 70.2%, and 69.4%, respectively. It was found that all performance measures achieve over 87.5, 89.4, and 86.7% for the classification task using random forest classifier. These promising results suggest that the proposed technique can discriminate retinal images having laser marks and without laser marks, and has the potential to be an important constituent in computerized screening solution for retinal images. © Springer Nature Singapore Pte Ltd. 2020

关键词： Discrete wavelet transforms

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A Deep Learning-Based Pipeline for Multi-Class Motor imagery Problems with Small Portion of Labeled Datasets

A Deep Learning-Based Pipeline for Multi-Class Motor Imagery...

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Iranian Conference of Biomedical engineering (ICBME)

作者： Neda Abdollahpour Mohammadreza Yazdchi Zahra Baharlouei Department of Biomedical Engineering Ragheb Isfahani Institute of Higher Education Isfahan Iran Medical Image and Signal Processing Research Center School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Isfahan Iran

In this article, a new framework is proposed to address multi-class Motor imagery Brain-Computer Interface (MIBCI) problems containing a small portion of labeled datasets. In this framework, the combination of Independent Component Analysis (ICA), multi-class Common Spatial Pattern (CSP), and a functional Application Programming Interface (API) model assumes a pivotal role. In the feature extraction stage of the work, a concatenated altered signal affected by spatial weights is proposed for each trial in three frequency ranges. This distribution of features can both provide suitable feature maps for augmentation, preparing data for the deep learning analysis, and underscore distinguishable features of MI classes. In the classification stage, spatial and temporal features are dominated by using the effective combination of a one-dimensional Convolutional neural Network (CNN) and a two-staged Bidirectional Long Short-Term Memory (BLSTM) in three branches containing different distributions of frequency. Given that, the model simultaneously learns past-to-future and future-to-past patterns in two stages. The experimental result on datasets 2a BCI-Competition IV illustrates that the proposed method can be liable, practical and more competitive than the other popular methods pointed out in this paper. All in all, the proposed framework can alleviate the issue of small portions of labeled datasets in MI problems.

关键词： Deep learning Biological system modeling Pipelines Independent component analysis Feature extraction Brain modeling Brain-computer interfaces

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EEG Artifact Removal Based on Brain Dipoles' Regions Using ICA and Dipfit in Motor imagery Tasks

EEG Artifact Removal Based on Brain Dipoles' Regions Using I...

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Iranian Conference of Biomedical engineering (ICBME)

In this article, a new semi-automatic Electroencephalogram (EEG) artifact removal has been proposed for Motor imagery (MI) tasks to improve the system's performance. There are eight reference clusters whose locations have been calculated based on the precise coordinates of a copious amount of brain dipoles acquired from a large number of users performing MI tasks. In this method, called 8 Ref-Clusters, a kind of Blind Source Separation (BSS) algorithm along with the DIPFIT plugin of the EEGLAB platform take on a decisive role. These eight clusters demonstrate which dipoles are brain sources and which ones are artifacts to eliminate. In the case of improving the performance of a system for a particular subject, we defined a specific threshold that could alter the size of clusters in three dimensions. The elaborate threshold pointed out above is unquestionably user-dependent. Having made a comparison between results before and after applying the 8-Ref-Clusters on the BCI-Competition IV 2a datasets, the average performance increased by roughly 4% which is promising when the datasets used in these evaluations had been filtered, between 8 and 30 Hz, only before applying Independent Component Analysis (ICA). Making a comparison between the results of the proposed method and those of other CSP-based methods shows that applying the proposed artifact removal only before CSP can significantly enhance the performance of the system at about 15.6%, in the case of the mCSP method. All in all, the proposed artifact removal method is a semi-automatic method that is computationally fast and able to detect the various types of artifacts like a heartbeat, muscle and head movements, eye blink, line noise, and so on.

关键词： Training Head Heart beat System performance Independent component analysis Muscles Lead

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zijian Zhu Yichi Zhang Hai Chen Yinpeng Dong Shu Zhao Wenbo Ding Jiachen Zhong Shibao Zheng Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Dept. of Comp. Sci. and Tech. THBI Lab Institute for AI Tsinghua University BNRist Center Zhongguancun Laboratory Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Computer Science and Technology Anhui University SAIC Motor AI Lab

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations; 2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features; 3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models.

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