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Speaker Extraction with Verification of Present and Absent Target Speakers

Journal of Shanghai Jiaotong University (Science) 2025年 1-6页

作者： Zhang, Ke Borsdorf, Marvin Liu, Tianchi Wang, Shuai Wei, Yangjie Li, Haizhou Key Laboratory of Intelligent Computing in Medical Image Northeastern University Shenyang110819 China Shenzhen Guangdong518000 China Machine Listening Lab University of Bremen Bremen28359 Germany Department of Electrical and Computer Engineering National University of Singapore Singapore119077 Singapore

Target speaker extraction (TSE) models are expected to extract the target speech from a cocktail party mixture signal. When only trained with present target speaker samples (PT), these models output noise in the absence of the target speaker (AT). One may enhance the TSE quality by providing the information about the PT and AT. However, the detection of the target speaker is not perfect. In this paper, we propose a new model, TSEV, which performs target speaker extraction and speaker verification simultaneously. The TSEV model outputs an extracted speech and generates two speaker embeddings per inference to detect the target speaker. By sharing the speaker encoder and low-level modules, the speaker verification task can be performed in low signal-to-noise ratio scenarios. We train the TSEV model on multi-talker PT and AT conditions with fully overlapped speech. Experiments verify the superiority of jointly performing two tasks in the proposed model. The TSEV model achieves better verification performance without degrading the extraction performance compared with the baseline. © Shanghai Jiao Tong University 2025.

关键词： Speech recognition

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Salient Region Matching for Fully Automated MR-TRUS Registration 22

Salient Region Matching for Fully Automated MR-TRUS Registra...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Feng, Zetian Ni, Dong Wang, Yi School of Biomedical Engineering Shenzhen University Medical School Shenzhen University The Medical UltraSound Image Computing (MUSIC) Lab The Smart Medical Imaging Learning and Engineering (SMILE) Lab Shenzhen China

ISBN: (纸本)9798331520526

Prostate cancer is a leading cause of cancer-related mortality in men. The registration of magnetic resonance (MR) and transrectal ultrasound (TRUS) can provide guidance for the targeted biopsy of prostate cancer. In this study, we propose a salient region matching framework for fully automated MR-TRUS registration. The framework consists of prostate segmentation, rigid alignment and deformable registration. Prostate segmentation is performed using two segmentation networks on MR and TRUS respectively, and the predicted salient regions are used for the rigid alignment. The rigidly-aligned MR and TRUS images serve as initialization for the deformable registration. The deformable registration network has a dual-stream encoder with cross-modal spatial attention modules to facilitate multi-modality feature learning, and a salient region matching loss to consider both structure and intensity similarity within the prostate region. Experiments on a public MR-TRUS dataset demonstrate that our method achieves satisfactory registration results, outperforming several cutting-edge methods. The code is publicly available at https://***/mock/ngbrd/salient-region-matching. © 2025 IEEE.

关键词： deformable registration MR-TRUS registration multi-modality registration prostate cancer

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FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide image Classification 38

FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Fu, Kexue Luo, Xiaoyuan Qu, Linhao Wang, Shuo Xiong, Ying Maglogiannis, Ilias Gao, Longxiang Wang, Manning Qilu University of Technology Shandong Academy of Sciences Jinan China Digital Medical Research Center School of Basic Medical Sciences Fudan University China Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention China Fudan University China University of Piraeus Greece Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

The expensive fine-grained annotation and data scarcity have become the primary obstacles for the widespread adoption of deep learning-based Whole Slide images (WSI) classification algorithms in clinical practice. Unlike few-shot learning methods in natural images that can leverage the labels of each image, existing few-shot WSI classification methods only utilize a small number of fine-grained labels or weakly supervised slide labels for training in order to avoid expensive fine-grained annotation. They lack sufficient mining of available WSIs, severely limiting WSI classification performance. To address the above issues, we propose a novel and efficient dual-tier few-shot learning paradigm for WSI classification, named FAST. FAST consists of a dual-level annotation strategy and a dual-branch classification framework. Firstly, to avoid expensive fine-grained annotation, we collect a very small number of WSIs at the slide level, and annotate an extremely small number of patches. Then, to fully mining the available WSIs, we use all the patches and available patch labels to build a cache branch, which utilizes the labeled patches to learn the labels of unlabeled patches and through knowledge retrieval for patch classification. In addition to the cache branch, we also construct a prior branch that includes learnable prompt vectors, using the text encoder of visual-language models for patch classification. Finally, we integrate the results from both branches to achieve WSI classification. Extensive experiments on binary and multi-class datasets demonstrate that our proposed method significantly surpasses existing few-shot classification methods and approaches the accuracy of fully supervised methods with only 0.22% annotation costs. All codes and models will be publicly available on https://***/fukexue/FAST. © 2024 Neural information processing systems foundation. All rights reserved.

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Heterogeneous model ensemble for automatic polyp segmentation in endoscopic video sequences 4

Heterogeneous model ensemble for automatic polyp segmentatio...

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4th International Workshop and Challenge on Computer Vision in Endoscopy, EndoCV 2022

作者： Tran, Thuy Nuong Isensee, Fabian Krämer, Lars Yamlahi, Amine Adler, Tim Godau, Patrick Tizabi, Minu Maier-Hein, Lena Heidelberg Germany Div. Medical Image Computing DKFZ Heidelberg Germany Applied Computer Vision Lab Helmholtz Imaging Germany

The detection and segmentation of polyps during colonoscopy can substantially contribute to the prevention of colon cancer. Assisting clinicians using automated systems can mitigate the risk of human error. In this work, we present our polyp segmentation approach, submitted to the EndoCV2022 challenge. Common polyp segmentation methods are based on single-model, single-frame predictions. This work presents a symbiosis of three separate models, each with their own strength, as part of a segmentation pipeline and a post-processing step designed to leverage unique predictions for more temporally coherent results. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Heterogeneous ensemble High resolution Polyp segmentation Temporal coherence

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Robust and Faster Zeroth-Order Minimax Optimization: Complexity and Applications 38

Robust and Faster Zeroth-Order Minimax Optimization: Complex...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： An, Weixin Liu, Yuanyuan Shang, Fanhua Liu, Hongying Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University China College of Intelligence and Computing Tianjin University China Medical School Tianjin University China Peng Cheng Lab Shenzhen China

Many zeroth-order (ZO) optimization algorithms have been developed to solve nonconvex minimax problems in machine learning and computer vision areas. However, existing ZO minimax algorithms have high complexity and rely on some strict restrictive conditions for ZO estimations. To address these issues, we design a new unified ZO gradient descent extragradient ascent (ZO-GDEGA) algorithm, which reduces the overall complexity to O(dϵ-6) to find an ϵ-stationary point of the function ψ for nonconvex-concave (NC-C) problems, where d is the variable dimension. To the best of our knowledge, ZO-GDEGA is the first ZO algorithm with complexity guarantees to solve stochastic NC-C problems. Moreover, ZO-GDEGA requires weaker conditions on the ZO estimations and achieves more robust theoretical results. As a by-product, ZO-GDEGA has advantages on the condition number for the NC-strongly concave case. Experimentally, ZO-GDEGA can generate more effective poisoning attack data with an average accuracy reduction of 5%. The improved AUC performance also verifies the robustness of gradient estimations. © 2024 Neural information processing systems foundation. All rights reserved.

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Plain-to-clear speech video conversion for enhanced intelligibility

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International Journal of Speech Technology 2023年第1期26卷 163-184页

作者： Sachdeva, Shubam Ruan, Haoyao Hamarneh, Ghassan Behne, Dawn M. Jongman, Allard Sereno, Joan A. Wang, Yue Language and Brain Lab Department of Linguistics Simon Fraser University BurnabyBC Canada Medical Image Analysis Research Group School of Computing Science Simon Fraser University BurnabyBC Canada NTNU Speech Lab Department of Psychology Norwegian University of Science and Technology Trondheim Norway KU Phonetics and Psycholinguistics Lab Department of Linguistics University of Kansas LawrenceKS United States

Clearly articulated speech, relative to plain-style speech, has been shown to improve intelligibility. We examine if visible speech cues in video only can be systematically modified to enhance clear-speech visual features and improve intelligibility. We extract clear-speech visual features of English words varying in vowels produced by multiple male and female talkers. Via a frame-by-frame image-warping based video generation method with a controllable parameter (displacement factor), we apply the extracted clear-speech visual features to videos of plain speech to synthesize clear speech videos. We evaluate the generated videos using a robust, state of the art AI Lip Reader as well as human intelligibility testing. The contributions of this study are: (1) we successfully extract relevant visual cues for video modifications across speech styles, and have achieved enhanced intelligibility for AI;(2) this work suggests that universal talker-independent clear-speech features may be utilized to modify any talker’s visual speech style;(3) we introduce "displacement factor" as a way of systematically scaling the magnitude of displacement modifications between speech styles;and (4) the high definition generated videos make them ideal candidates for human-centric intelligibility and perceptual training studies. © 2023, The Author(s).

关键词： Speech synthesis

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Salient Region Matching for Fully Automated MR-TRUS Registration

Salient Region Matching for Fully Automated MR-TRUS Registra...

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IEEE International Symposium on Biomedical Imaging

作者： Zetian Feng Dong Ni Yi Wang the Medical UltraSound Image Computing (MUSIC) Lab and the Smart Medical Imaging Learning and Engineering (SMILE) Lab School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

关键词： Representation learning image segmentation Ultrasonic imaging Codes Biopsy Mortality Magnetic resonance Prostate cancer Biomedical imaging

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DISENTANGLED PET LESION SEGMENTATION

arXiv

引用

arXiv 2024年

作者： Gatsak, Tanya Abhishek, Kumar Yedder, Hanene Ben Taghanaki, Saeid Asgari Hamarneh, Ghassan Medical Image Analysis Lab School of Computing Science Simon Fraser University Canada Autodesk AI Research Canada

PET imaging is an invaluable tool in clinical settings as it captures the functional activity of both healthy anatomy and cancerous lesions. Developing automatic lesion segmentation methods for PET images is crucial since manual lesion segmentation is laborious and prone to inter- and intra-observer variability. We propose PET-Disentangler, a 3D disentanglement method that uses a 3D UNet-like encoder-decoder architecture to disentangle disease and normal healthy anatomical features with losses for segmentation, reconstruction, and healthy component plausibility. A critic network is used to encourage the healthy latent features to match the distribution of healthy samples and thus encourages these features to not contain any lesion-related features. Our quantitative results show that PET-Disentangler is less prone to incorrectly declaring healthy and high tracer uptake regions as cancerous lesions, since such uptake pattern would be assigned to the disentangled healthy component. Copyright © 2024, The Authors. All rights reserved.

关键词： image segmentation

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Local Implicit Wavelet Transformer for Arbitrary-Scale Super-Resolution

arXiv

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arXiv 2024年

作者： Duan, Minghong Qu, Linhao Liu, Shaolei Wang, Manning Digital Medical Research Center Fudan University Shanghai China Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention China

Implicit neural representations have recently demonstrated promising potential in arbitrary-scale Super-Resolution (SR) of images. Most existing methods predict the pixel in the SR image based on the queried coordinate and ensemble nearby features, overlooking the importance of incorporating high-frequency prior information in images, which results in limited performance in reconstructing high-frequency texture details in images. To address this issue, we propose the Local Implicit Wavelet Transformer (LIWT) to enhance the restoration of high-frequency texture details. Specifically, we decompose the features extracted by an encoder into four sub-bands containing different frequency information using Discrete Wavelet Transform (DWT). We then introduce the Wavelet Enhanced Residual Module (WERM) to transform these four sub-bands into high-frequency priors, followed by utilizing the Wavelet Mutual Projected Fusion (WMPF) and the Wavelet-aware Implicit Attention (WIA) to fully exploit the high-frequency prior information for recovering high-frequency details in images. We conducted extensive experiments on benchmark datasets to validate the effectiveness of LIWT. Both qualitative and quantitative results demonstrate that LIWT achieves promising performance in arbitrary-scale SR tasks, outperforming other state-of-the-art methods. The code is available at https://***/dmhdmhdmh/LIWT. Copyright © 2024, The Authors. All rights reserved.

关键词： Discrete wavelet transforms

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Hierarchical Agent-Based Reinforcement Learning Framework for Automated Quality Assessment of Fetal Ultrasound Video

Hierarchical Agent-Based Reinforcement Learning Framework fo...

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IEEE International Symposium on Biomedical Imaging

作者： Sijing Liu Qilong Ying Shuangchi He Xin Yang Dong Ni Ruobing Huang Medical Ultrasound Image Computing (MUSIC) Lab Health Science Center Shenzhen University China

Ultrasound is the primary modality to examine fetal growth during pregnancy, while the image quality could be affected by various factors. Quality assessment is essential for controlling the quality of ultrasound images to guarantee both the perceptual and diagnostic values. Existing automated approaches often require heavy structural annotations and the predictions may not necessarily be consistent with the assessment results by human experts. Furthermore, the overall quality of a scan and the correlation between the quality of frames should not be overlooked. In this work, we propose a reinforcement learning framework powered by two hierarchical agents that collaboratively learn to perform both frame-level and video-level quality assessments. It is equipped with a specially-designed reward mechanism that considers temporal dependency among frame quality and only requires sparse binary annotations to train. Experimental results on a challenging fetal brain dataset verify that the proposed framework could perform dual-level quality assessment and its predictions correlate well with the subjective assessment results.

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