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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Yu Zhou Xiahao Zou Yi Wang Smart Medical Imaging Learning and Engineering (SMILE) Lab Medical UltraSound Image Computing (MUSIC) Lab School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Bone segmentation is an essential step for the preoperative planning of fracture trauma surgery. The automated segmentation of fractured bone from computed tomography (CT) scans remains challenging, due to the large differences of fractures in position and morphology, and also the inherent anatomical characteristics of different bone structures. To alleviate these issues, we propose a cross-scale attention mechanism as well as a surface supervision strategy for fractured bone segmentation in CT. Specifically, a cross-scale attention mechanism is introduced to effectively aggregate the features among different scales to provide more powerful fracture representation. Moreover, a surface supervision strategy is employed, which explicitly constrains the network to pay more attention to the bone boundary. The efficacy of the proposed method is evaluated on a public dataset containing CT scans with hip fractures. The evaluation metrics are Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), and Hausdorff distance (95HD). The proposed method achieves an average DSC of 93.36%, ASSD of 0.85mm, 95HD of 7.51mm. Our method offers an effective fracture segmentation approach for the pelvic CT examinations, and has the potential to be used for improving the segmentation performance of other types of fractures. Our code is publicly available at https://***/ZhouyuPOP/FracSeg-Net.

关键词： image segmentation Attention mechanisms Computed tomography Semantics Surface morphology Surgery Bones Surface cracks Surface treatment Hip

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Pyramid Attention Network for medical image Registration

Pyramid Attention Network for Medical Image Registration

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IEEE International Symposium on Biomedical Imaging

作者： Zhuoyuan Wang Haiqiao Wang Yi Wang School of Biomedical Engineering Shenzhen University Shenzhen China The Medical UltraSound Image Computing (MUSIC) Lab Shenzhen China The Smart Medical Imaging Learning and Engineering (SMILE) Lab Shenzhen China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

The advent of deep-learning-based registration networks has addressed the time-consuming challenge in traditional iterative methods. However, the potential of current registration networks for comprehensively capturing spatial relationships has not been fully explored, leading to inadequate performance in large-deformation image registration. The pure convolutional neural networks (CNNs) neglect feature enhancement, while current Transformer-based networks are susceptible to information redundancy. To alleviate these issues, we propose a pyramid attention network (PAN) for deformable medical image registration. Specifically, the proposed PAN incorporates a dual-stream pyramid encoder with channel-wise attention to boost the feature representation. Moreover, a multi-head local attention Transformer is introduced as decoder to analyze motion patterns and generate deformation fields. Extensive experiments on two public brain magnetic resonance imaging (MRI) datasets and one abdominal MRI dataset demonstrate that our method achieves favorable registration performance, while outperforming several CNN-based and Transformer-based registration networks. Our code is publicly available at https://***/JuliusWang-7/PAN.

关键词： image registration Deformation Current transformers Magnetic resonance imaging Termination of employment Estimation Decoding

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Fusionmlp: A Mlp-Based Unified image Fusion Framework

SSRN

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SSRN 2024年

作者： Liu, Shaolei Li, Shiman Qu, Linhao Wang, Manning Song, Zhijian Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai200032 China Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention China

Due to the powerful feature representation capacity, deep learning-based image fusion methods have improved the fusion results for better information integration. However, some inherent limitations in convolutional neural networks (CNN) and Transformer networks impede further performance improvement. Specifically, CNN-based methods have difficulties in mining global context information;Transformer-based methods are unable to adapt to different image sizes and the computational complexity grows exponentially with the image size. To overcome these problems, we propose the first MLP-based multi-scene image fusion framework, denoted as FusionMLP. In addition, we propose a novel loss function combining the spatial and frequency domain information for model training. Extensive experiments show that FusionMLP not only outperform SOTA multi-scene image fusion methods, but also outperform SOTA fusion algorithms for specific scenes. © 2024, The Authors. All rights reserved.

关键词： image fusion

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Boosting Whole Slide image Classification from the Perspectives of Distribution, Correlation and Magnification

Boosting Whole Slide Image Classification from the Perspecti...

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International Conference on Computer Vision (ICCV)

作者： Linhao Qu Zhiwei Yang Minghong Duan Yingfan Ma Shuo Wang Manning Wang Zhijian Song Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention

Bag-based multiple instance learning (MIL) methods have become the mainstream for Whole Slide image (WSI) classification. However, there are still three important issues that have not been fully addressed: (1) positive bags with a low positive instance ratio are prone to the influence of a large number of negative instances; (2) the correlation between local and global features of pathology images has not been fully modeled; and (3) there is a lack of effective information interaction between different magnifications. In this paper, we propose MILBooster, a powerful dual-scale multi-stage MIL framework to address these issues from the perspectives of distribution, correlation, and magnification. Specifically, to address issue (1), we propose a plug-and-play bag filter that effectively increases the positive instance ratio of positive bags. For issue (2), we propose a novel window-based Transformer architecture called PiceBlock to model the correlation between local and global features of pathology images. For issue (3), we propose a dual-branch architecture to process different magnifications and design an information interaction module called Scale Mixer for efficient information interaction between them. We conducted extensive experiments on four clinical WSI classification tasks using three datasets. MILBooster achieved new state-of-the-art performance on all these tasks. Codes will be available at https://***/miccaiif/MILBooster.

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The rise of AI language pathologists: exploring two-level prompt learning for few-shot weakly-supervised whole slide image classification 23

The rise of AI language pathologists: exploring two-level pr...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Linhao Qu Xiaoyuan Luo Kexue Fu Manning Wang Zhijian Song Digital Medical Research Center School of Basic Medical Science Fudan University and Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention

This paper introduces the novel concept of few-shot weakly supervised learning for pathology Whole Slide image (WSI) classification, denoted as FSWC. A solution is proposed based on prompt learning and the utilization of a large language model, GPT-4. Since a WSI is too large and needs to be divided into patches for processing, WSI classification is commonly approached as a Multiple Instance Learning (MIL) problem. In this context, each WSI is considered a bag, and the obtained patches are treated as instances. The objective of FSWC is to classify both bags and instances with only a limited number of labeled bags. Unlike conventional few-shot learning problems, FSWC poses additional challenges due to its weak bag labels within the MIL framework. Drawing inspiration from the recent achievements of vision-language models (V-L models) in downstream few-shot classification tasks, we propose a two-level prompt learning MIL framework tailored for pathology, incorporating language prior knowledge. Specifically, we leverage CLIP to extract instance features for each patch, and introduce a prompt-guided pooling strategy to aggregate these instance features into a bag feature. Subsequently, we employ a small number of labeled bags to facilitate few-shot prompt learning based on the bag features. Our approach incorporates the utilization of GPT-4 in a question-and-answer mode to obtain language prior knowledge at both the instance and bag levels, which are then integrated into the instance and bag level language prompts. Additionally, a learnable component of the language prompts is trained using the available few-shot labeled data. We conduct extensive experiments on three real WSI datasets encompassing breast cancer, lung cancer, and cervical cancer, demonstrating the notable performance of the proposed method in bag and instance classification. Codes will be available at https://***/miccaiif/TOP.

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Deep Mutual Learning among Partially labeled Datasets for Multi-Organ Segmentation

arXiv

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arXiv 2024年

作者： Liu, Xiaoyu Qu, Linhao Xie, Ziyue Shi, Yonghong Song, Zhijian Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai200032 China Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China

The task of labeling multiple organs for segmentation is a complex and time-consuming process, resulting in a scarcity of comprehensively labeled multi-organ datasets while the emergence of numerous partially labeled datasets. Current methods are inadequate in effectively utilizing the supervised information available from these datasets, thereby impeding the progress in improving the segmentation accuracy. This paper proposes a two-stage multi-organ segmentation method based on mutual learning, aiming to improve multi-organ segmentation performance by complementing information among partially labeled datasets. In the first stage, each partial-organ segmentation model utilizes the non-overlapping organ labels from different datasets and the distinct organ features extracted by different models, introducing additional mutual difference learning to generate higher quality pseudo labels for unlabeled organs. In the second stage, each full-organ segmentation model is supervised by fully labeled datasets with pseudo labels and leverages true labels from other datasets, while dynamically sharing accurate features across different models, introducing additional mutual similarity learning to enhance multi-organ segmentation performance. Extensive experiments were conducted on nine datasets that included the head and neck, chest, abdomen, and pelvis. The results indicate that our method has achieved SOTA performance in segmentation tasks that rely on partial labels, and the ablation studies have thoroughly confirmed the efficacy of the mutual learning mechanism. © 2024, CC BY.

关键词： Deep learning

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FANCL: Feature-Guided Attention Network with Curriculum Learning for Brain Metastases Segmentation

arXiv

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arXiv 2024年

作者： Liu, Zijiang Liu, Xiaoyu Qu, Linhao Shi, Yonghong Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai200032 China Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China

Accurate segmentation of brain metastases (BMs) in MR image is crucial for the diagnosis and followup of patients. Methods based on deep convolutional neural networks (CNNs) have achieved high segmentation performance. However, due to the loss of critical feature information caused by convolutional and pooling operations, CNNs still face great challenges in small BMs segmentation. Besides, BMs are irregular and easily confused with healthy tissues, which makes it difficult for the model to effectively learn tumor structure during training. To address these issues, this paper proposes a novel model called feature-guided attention network with curriculum learning (FANCL). Based on CNNs, FANCL utilizes the input image and its feature to establish the intrinsic connections between metastases of different sizes, which can effectively compensate for the loss of high-level feature from small tumors with the information of large tumors. Furthermore, FANCL applies the voxel-level curriculum learning strategy to help the model gradually learn the structure and details of BMs. And baseline models of varying depths are employed as curriculum-mining networks for organizing the curriculum progression. The evaluation results on the BraTS-METS 2023 dataset indicate that FANCL significantly improves the segmentation performance, confirming the effectiveness of our method. Copyright © 2024, The Authors. All rights reserved.

关键词： Curricula

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The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide image Classification

arXiv

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arXiv 2023年

作者： Qu, Linhao Luo, Xiaoyuan Fu, Kexue Wang, Manning Song, Zhijian Digital Medical Research Center School of Basic Medical Science Fudan University China Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention China

This paper introduces the novel concept of few-shot weakly supervised learning for pathology Whole Slide image (WSI) classification, denoted as FSWC. A solution is proposed based on prompt learning and the utilization of a large language model, GPT-4. Since a WSI is too large and needs to be divided into patches for processing, WSI classification is commonly approached as a Multiple Instance Learning (MIL) problem. In this context, each WSI is considered a bag, and the obtained patches are treated as instances. The objective of FSWC is to classify both bags and instances with only a limited number of labeled bags. Unlike conventional few-shot learning problems, FSWC poses additional challenges due to its weak bag labels within the MIL framework. Drawing inspiration from the recent achievements of vision-language models (V-L models) in downstream few-shot classification tasks, we propose a two-level prompt learning MIL framework tailored for pathology, incorporating language prior knowledge. Specifically, we leverage CLIP to extract instance features for each patch, and introduce a prompt-guided pooling strategy to aggregate these instance features into a bag feature. Subsequently, we employ a small number of labeled bags to facilitate few-shot prompt learning based on the bag features. Our approach incorporates the utilization of GPT-4 in a question- and-answer mode to obtain language prior knowledge at both the instance and bag levels, which are then integrated into the instance and bag level language prompts. Additionally, a learnable component of the language prompts is trained using the available few-shot labeled data. We conduct extensive experiments on three real WSI datasets encompassing breast cancer, lung cancer, and cervical cancer, demonstrating the notable performance of the proposed method in bag and instance classification. Codes will be available at https://***/miccaiif/TOP. Copyright © 2023, The Authors. All rights reserved.

关键词： Pathology

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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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Fusionmlp: A Mlp-Based Unified image Fusion Framework

SSRN

引用

SSRN 2023年

作者： Liu, Shaolei Qu, Linhao Wang, Manning Song, Zhijian Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai200032 China Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention China

Due to the powerful feature representation capacity, deep learning-based image fusion methods have improved the fusion results for better information integration. However, some inherent limitations in convolutional neural networks (CNN) and Transformer networks impede further performance improvement. Specifically, CNN-based methods have difficulties in mining global context information;Transformer-based methods are unable to adapt to different image sizes and the computational complexity grows exponentially with the image size. To overcome these problems, we propose the first MLPbased multi-scene image fusion framework, denoted as FusionMLP. In addition, we propose a novel loss function combining the spatial and frequency domain information for model training. Extensive experiments show that FusionMLP not only outperform SOTA multi-scene image fusion methods, but also outperform SOTA fusion algorithms for specific scenes. Codes will be released at https://***/slliuEric/FusionMLP. © 2023, The Authors. All rights reserved.

关键词： image fusion

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