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International Conference on Biomimetic Intelligence and Robotics & medical Robotics Forum, ICBIR+MRF 2024

作者： Chen, Yijia Li, Jiapeng Yu, Haoze Qi, Lin Li, Yongchun College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China Engineering Research Center of Medical Imaging and Intelligent Analysis Ministry of Education Shenyang110169 China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang110169 China Shenyang Contain Electronic Technology Co. Ltd. Shenyang110167 China

This research focuses on fundus image segmentation within a source-free domain adaptation framework, where the availability of source images during the adaptation phase is limited due to privacy concerns. Although Source-Free Unsupervised Domain Adaptation (SFUDA) methods have seen significant innovative developments in recent years, they still face several challenges which include suboptimal performance due to substantial domain discrepancies, reliance on potentially noisy or inaccurate pseudo-labels during the adaptation process, and a lack of integration with domain-specific prior knowledge. To address these issues, this paper proposes a SFUDA framework via Entropy Optimization and Anatomical Priors (EOAPNet). To alleviate the influence of the divergence between the source and target domains, EOAPNet primarily evaluates the uncertainty (i.e., entropy) of predictions on target domain data and improves the model by focusing on high-entropy pixels or regions. Additionally, a weak-strong augmentation mean-teacher scheme is introduced in EOAPNet, which can enhance the accuracy of pseudo-labels and reduce error propagation. Thirdly, by integrating an anatomical knowledge-based class ratio prior into the overall loss function in the form of a Kullback-Leibler (KL) divergence, EOAPNet also incorporates expert domain knowledge. EOAPNet yields comparable results to several state-of-the-art adaptation techniques in experiments on two retinal image segmentation datasets involving the RIM-ONE-r3 and Drishti-GS datasets. © 2024 The Authors.

关键词： Domain Knowledge

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Cdunet: A U-Shaped Encoder-Decoder Network with Channel Attention Block and Dynamic Feature Extraction Block for Ldct Denoising

SSRN

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SSRN 2024年

作者： Li, Minghao Teng, Yueyang Zhou, Jie Li, Mingrui Zhu, Ruiming Wang, Haoran College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110169 China

Background and Objective: Computer tomography (CT) has become an important tool of clinical diagnosis. However, X-ray radiation related to CT can cause harm to patients. Low-dose CT (LDCT) can reduce radiation, however it can bring noise. How to remove noise from LDCT is a worthwhile research topic. In recent years, deep learning has shown outstanding performance in the field of LDCT ***: This article proposes a U-shaped encoder-decoder network CDUNet with channel attention block (CAB) and dynamic feature extraction block (DFEB). The CAB assigns different weights to feature maps to better utilize multi-channel information. The DFEB uses CondConv, which is able to respond differently to different inputs. When processing CT images, DEFB can perform targeted processing on different CT slices and different parts within each slice to achieve better feature extraction. At the same time, we also use the Sobel operator to calculate the gradient of the image, combining gradient loss with Mean Square Error (MAE) loss to alleviate the smoothing problem and restore the structural information of the image ***: The experiments on the 2016 NIH-AAPM-Mayo Clinic LDCT Grand Challenge show that our method has better results compared with state-of-the-art (SOTA) methods. Specifically, our method not only has the best visual effects, but also outperforms other algorithms in terms of evaluation ***: Experiments shows that our method is reasonable and effective. The code is available at https://***/xxxxxxmmmmmm/CDUNet. © 2024, The Authors. All rights reserved.

关键词： Network coding

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SETTP: Style Extraction and Tunable Inference via Dual-Level Transferable Prompt Learning 27

SETTP: Style Extraction and Tunable Inference via Dual-Level...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Jin, Chunzhen Huang, Yongfeng Wang, Yaqi Cao, Peng Zaïane, Osmar Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China Amii University of Alberta EdmontonAB Canada

ISBN: (纸本)9781643685489

Text style transfer, an important research direction in natural language processing, aims to adapt the text to various preferences but often faces challenges with limited resources. In this work, we introduce a novel method termed Style Extraction and Tunable Inference via Dual-level Transferable Prompt Learning (SETTP) for effective style transfer in low-resource scenarios. First, SETTP learns source style-level prompts containing fundamental style characteristics from high-resource style transfer. During training, the source style-level prompts are transferred through an attention module to derive a target style-level prompt for beneficial knowledge provision in low-resource style transfer. Additionally, we propose instance-level prompts obtained by clustering the target resources based on the semantic content to reduce semantic bias. We also propose an automated evaluation approach of style similarity based on alignment with human evaluations using ChatGPT-4. Our experiments across three resourceful styles show that SETTP requires only 1/20th of the data volume to achieve performance comparable to state-of-the-art methods. In tasks involving scarce data like writing style and role style, SETTP outperforms previous methods by 16.24%. © 2024 The Authors.

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Positive-Unlabeled Learning with Field of View Consistency for Histology image Segmentation 1

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13th International Conference on Frontier computing, FC 2023

作者： Jia, Xiaoqi Fu, Chong Hou, Jiaxin Qin, Wenjian School of Computer Science and Engineering Northeastern University Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China

ISBN: (数字)9789819993420

ISBN: (纸本)9789819993413

Histology image annotation is costly and time-consuming. Utilizing Positive and Unlabeled (PU) data for model training offers a more resource-efficient alternative. However, previous methods for PU learning suffer from the noise arising from label assignment to unlabeled data. We observe that predictions on noisy data lack consistency under data augmentation. In this paper, we present Field of View (FoV) consistency regularization for PU segmentation in histology images, which effectively reduces the noise influence by promoting consistent predictions across varying FoVs. Using only 20% of positive labels on the Glas Dataset, our approach outperforms previous methods, achieving a Dice score of 90.69%-almost reaching the fully supervised result of 93.30%. Source code is available at: https://***/lzaya/PU_with_FoV. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： image segmentation

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Multi-Level Speaker Representation for Target Speaker Extraction

Multi-Level Speaker Representation for Target Speaker Extrac...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Ke Li, Junjie Wang, Shuai Wei, Yangjie Wang, Yi Wang, Yannan Li, Haizhou Key Laboratory of Intelligent Computing in Medical Image Northeastern University China SDS SRIBD The Chinese University of Hong Kong Shenzhen China The Hong Kong Polytechnic University Hong Kong Tencent Ethereal Audio Lab Tencent Shenzhen China University of Bremen Germany Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (纸本)9798350368741

Target speaker extraction (TSE) relies on a reference cue of the target to extract the target speech from a speech mixture. While a speaker embedding is commonly used as the reference cue, such embedding pre-trained with a large number of speakers may suffer from confusion of speaker identity. In this work, we propose a multi-level speaker representation approach, from raw features to neural embeddings, to serve as the speaker reference cue. We generate a spectral-level representation from the enrollment magnitude spectrogram as a raw, low-level feature, which significantly improves the model's generalization capability. Additionally, we propose a contextual embedding feature based on cross-attention mechanisms that integrate frame-level embeddings from a pre-trained speaker encoder. By incorporating speaker features across multiple levels, we significantly enhance the performance of the TSE model. Our approach achieves a 2.74 dB improvement and a 4.94% increase in extraction accuracy on Libri2mix test set over the baseline. © 2025 IEEE.

关键词： Cocktail party problem selective auditory attention speaker confusion speaker feature target speaker extraction

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Subcortical Brain Segmentation with Convolutional Neural Networks 14

Subcortical Brain Segmentation with Convolutional Neural Net...

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14th International Conference on Digital image Processing, ICDIP 2022

作者： Gong, Zhaoxuan Chen, Xianyu Guo, Wei Ju, Ronghui Tan, Wenjun Zhou, Wei Zhang, Guodong Shenyang Aerospace University Liaoning Shenyang China Liaoning provincial people’s hospital Liaoning Shenyang China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Liaoning Shenyang China

ISBN: (纸本)9781510657564

Subcortical brain segmentation is a challenging task due to the anatomical variability in both shape and size between patients, such as thalamus, hippocampus and amygdala. It requires the accurate segmentation of these structures to measure their volume and surface. However, few methods can obtain accurate segmentation because the boundaries of these structures are obscure in MR images. We propose an attention-based convolutional neural network for subcortical brain segmentation. In our method, image clipping is firstly applied for pre-processing. Accurate subcortical brain segmentation is obtained by using attention-based convolutional neural network. Maximum connectivity is finally applied for post-processing. Experimental results in 35 subjects showed that the proposed method segment the brain region with higher accuracy than other methods. The Dice, TPR and VD measures show that the proposed method is able to provide a precise and robust segmentation estimate. The proposed method is a suitable alternative to assist the manual subcortical brain segmentation task. © 2022 SPIE.

关键词： Convolutional neural networks

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Application of graph-curvature features in computer-aided diagnosis for histopathological image identification of gastric cancer

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intelligent Medicine 2024年第3期4卷 141-152页

作者： Ruilin He Chen Li Xinyi Yang Jinzhu Yang Tao Jiang Marcin Grzegorzek Hongzan Sun Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information EngineeringNortheastern UniversityShenyangLiaoning 110016China Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyangLiaoning 110016China School of Intelligent Medicine Chengdu University of Traditional Chinese MedicineChengduSichuan 610075China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information TechnologyChengduSichuan 610225China Institute for Medical Informatics University of LuebeckLuebeckGermany Department of Knowledge Engineering University of Economics in KatowiceKatowicePoland Shengjing Hospital of China Medical University ShenyangLiaoning 110136China

Background Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors;however,it has some *** study explored a computer-aided diagnostic method that can be used to identify benign and malignant gastric cancer using histopathological *** The most suitable process was selected through multiple experiments by comparing multiple meth-ods and features for ***,the U-net was applied to segment the ***,the nucleus was extracted from the segmented image,and the minimum spanning tree(MST)diagram structure that can cap-ture the topological information was *** third step was to extract the graph-curvature features of the histopathological image according to the MST ***,by inputting the graph-curvature features into the classifier,the recognition results for benign or malignant cancer can be *** During the experiment,we used various methods for *** the image segmentation stage,U-net,watershed algorithm,and Otsu threshold segmentation methods were *** found that the U-net method,combined with multiple indicators,was the most suitable for segmentation of histopathological *** the feature extraction stage,in addition to extracting graph-edge and graph-curvature features,several basic im-age features were extracted,including the red,green and blue feature,gray-level co-occurrence matrix feature,histogram of oriented gradient feature,and local binary pattern *** the classifier design stage,we exper-imented with various methods,such as support vector machine(SVM),random forest,artificial neural network,K nearest neighbors,VGG-16,and *** comparison and analysis,it was found that classifica-tion results with an accuracy of 98.57%can be obtained by inputting the graph-curvature feature into the SVM classifier.

关键词： Gastric cancer Graph-curvature feature image identification

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Dual-Granularity Contrastive Learning for Session-based Recommendation

arXiv

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arXiv 2023年

作者： Wang, Zihan Wu, Gang Wang, Haotong School of Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China

The data encountered by Session-based Recommendation System(SBRS) is typically highly sparse, which also serves as one of the bottlenecks limiting the accuracy of recommendations. So Contrastive Learning(CL) is applied in SBRS owing to its capability of improving embedding learning under the condition of sparse data. However, existing CL strategies are limited in their ability to enforce finer-grained (e.g., factor-level) comparisons and, as a result, are unable to capture subtle differences between instances. More than that, these strategies usually use item or segment dropout as a means of data augmentation which may result in sparser data and thus ineffective self-supervised signals. By addressing the two aforementioned limitations, we introduce a novel dual-granularity CL framework. Specifically, two extra augmentation views with different granularities are constructed and the embeddings learned by them are compared with those learned from original view to complete the CL tasks. At factor-level, we employ Disentangled Representation Learning to obtain finer-grained data, with which we can explore connections of items on latent factor independently and generate factor-level embeddings. At item-level, the star graph is deployed as the augmentation method. By setting an additional satellite node, non-adjacent nodes can establish additional connections through satellite nodes instead of reducing the connections of the original graph, so data sparsity can be avoided. Compare the learned embeddings of these two views with the learned embeddings of the original view to achieve CL at two granularities. Finally, the item-level and factor-level embeddings obtained are referenced to generate personalized recommendations for the user. The proposed model is validated through extensive experiments on two benchmark datasets, showcasing superior performance compared to existing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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Deformable registration framework for glioma images with absent correspondence based on auxiliary-image-aided intensity-consistency constraint

Deformable registration framework for glioma images with abs...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Tang, Kun Wang, Lihui Yang, Menglong Xu, Jingwen Cheng, Xinyu Zhang, Jian Zhu, Yuemin Wei, Hongjiang Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Guiyang China Univ Lyon Insa Lyon Cnrs Inserm Creatis Umr 5220 U1206 Lyon France Shanghai Jiao Tong University School of Biomedical Engineering Shanghai200240 China

ISBN: (纸本)9798350386226

Considering the tumor aggressive nature and the significant changes in anatomical structure, aligning the preoperative and follow up scans of glioma patients remains a challenge due to the presence of regions with absent correspondence. To address this challenge, this work proposed a novel bidirectional unsupervised deformable image registration framework for image pairs with missing correspondence based on an auxiliary-image-aided intensity-consistency constraint (ICC) strategy. Specifically, for any fixed and moving image pairs, we introduced an auxiliary image and warped it directly to fixed/moving image or warped it twice through a transition of moving/fixed image. By comparing the difference between these warped images, the weighting maps to identify and exclude regions with absent correspondence between fixed and moving image pairs can be generated. To verify the effectiveness of the proposed framework, we combined it with several deep learning-based registration models and tested it on BraTS-Reg challenge dataset, the results demonstrated that the proposed ICC strategy can improve the registration performance for all the models, with the improvement of average target registration error (TRE) and success rate (SR) being up to 44.9% and 66.7%, respectively. Comparing against the best existing forward-backward consistency strategy for dealing with missing correspondence registration, our auxiliary-image-aided ICC strategy can also decrease average TRE by 2.9%, demonstrating the superiority of the proposed framework. The present work is not limited to the glioma images, it can be used to address the registration problems for any image pairs with absent correspondence or inconsistent intensity. © 2024 IEEE.

关键词： image registration

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medical image registration and its application in retinal images:a review

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Visual computing for Industry,Biomedicine,and Art 2024年第1期7卷 142-164页

作者： Qiushi Nie Xiaoqing Zhang Yan Hu Mingdao Gong Jiang Liu Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Singapore Eye Research Institute Singapore 169856Singapore State Key Laboratory of Ophthalmology Optometry and Visual ScienceEye HospitalWenzhou Medical UniversityWenzhou 325027China

medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse informa-tion of images,which may be captured under different times,angles,or *** several surveys have reviewed the development of medical image registration,they have not systematically summarized the existing med-ical image registration *** this end,a comprehensive review of these methods is provided from traditional and deep-learning-based perspectives,aiming to help audiences quickly understand the development of medical image *** particular,we review recent advances in retinal image registration,which has not attracted much *** addition,current challenges in retinal image registration are discussed and insights and prospects for future research provided.

关键词： Computer-aided diagnosis medical image registration Deep learning Generative model Transformer Retina

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