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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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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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Intelligent Evaluation of the Course of Atrial Fibrillation Using Transfer Learning 15

Intelligent Evaluation of the Course of Atrial Fibrillation ...

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15th International Congress on image and Signal Processing, Biomedical Engineering and Informatics, CISP-BMEI 2022

作者： Sun, Chen Li, Chunpu Huo, Hongye Zhong, Gaoyan Liu, Sen Wang, Aiguo Deng, Xintao Yang, Cuiwei Fudan University Center for Biomedical Engineering Shanghai China Xinghua City People's Hospital Department of Cardiology Jiangsu China The Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai China

ISBN: (数字)9781665488877

ISBN: (纸本)9781665488877

Atrial fibrillation (AF) is the most common chronic cardiovascular disease. As the disease progresses, AF could have various impact on the quality of life and the health of patients. The scientific management of AF depends on the development of targeted therapeutic measures according to the disease course. In view of the noninvasiveness and convenience of electrocardiogram (ECG), this study attempted to assess the course of AF based on standard 12-lead ECG. Therefore, based on transfer learning, we used MobileNetV2 to achieve automatic grading of AF treatment. By taking 12-lead ECG images of patients with mild and severe AF as network input, the pre-trained model was retrained to recognize different courses. The results show that our proposed method exhibits excellent performance with accuracy of 84.1 %, precision of 86.5%, recall of 83.1%, specificity of 85.3%, and F1-score of 84.8%. Our study provides a feasible way of assessing the course of AF and is of great significance to promote the graded diagnosis and treatment of AF. © 2022 IEEE.

关键词： Electrocardiograms

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Continuous K-space Recovery Network with image Guidance for Fast MRI Reconstruction

arXiv

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arXiv 2024年

作者： Meng, Yucong Yang, Zhiwei Duan, Minghong Shi, Yonghong Song, Zhijian Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai200032 China The Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China Academy of Engineering and Technology Fudan University Shanghai200433 China

Magnetic resonance imaging (MRI) is a crucial tool for clinical diagnosis while facing the challenge of long scanning time. To reduce the acquisition time, fast MRI reconstruction aims to restore high-quality MRI images from the undersampled k-space. Existing methods typically train deep learning models to map the undersampled data to artifact-free MRI images. However, these studies often overlook the unique properties of k-space and directly apply general networks designed for image processing to k-space recovery, leaving the precise learning of k-space largely underexplored. In this work, we propose a continuous k-space recovery network from a new perspective of implicit neural representation with image domain guidance, which boosts the performance of MRI reconstruction. Specifically, (1) an implicit neural representation based encoder-decoder structure is customized to continuously query unsampled k-values. (2) an image guidance module is designed to mine the image-domain information from the low-quality MRI images to further guide the k-space recovery. (3) a multi-stage training strategy is proposed to recover dense k-space progressively. Extensive experiments conducted on CC359, fastMRI, IXI, and SKM-TEA datasets demonstrate the effectiveness of our method and its superiority over other competitors. Implementation code will be available in https://***/XiaoMengLiLiLi/IGKR. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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DM-Mamba: Dual-domain Multi-scale Mamba for MRI Reconstruction

arXiv

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arXiv 2025年

作者： Meng, Yucong Yang, Zhiwei Song, Zhijian Shi, Yonghong Fu, Kexue Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai200032 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China Academy of Engineering and Technology Fudan University Shanghai200433 China Jinan China

The accelerated MRI reconstruction poses a challenging ill-posed inverse problem due to the significant undersampling in k-space. Deep neural networks, such as CNNs and ViT, have shown substantial performance improvements for this task while encountering the dilemma between global receptive fields and efficient computation. To this end, this paper pioneers exploring Mamba, a new paradigm for long-range dependency modeling with linear complexity, for efficient and effective MRI reconstruction. However, directly applying Mamba to MRI reconstruction faces three significant issues: (1) Mamba’s row-wise and column-wise scanning disrupts k-space’s unique spectrum, leaving its potential in k-space learning unexplored. (2) Existing Mamba methods unfold feature maps with multiple lengthy scanning paths, leading to long-range forgetting and high computational burden. (3) Mamba struggles with spatially-varying contents, resulting in limited diversity of local representations. To address these, we propose a dual-domain multi-scale Mamba for MRI reconstruction from the following perspectives: (1) We pioneer vision Mamba in k-space learning. A circular scanning is customized for spectrum unfolding, benefiting the global modeling of k-space. (2) We propose a multi-scale Mamba with an efficient scanning strategy in both image and k-space domains. It mitigates long-range forgetting and achieves a better trade-off between efficiency and performance. (3) We develop a local diversity enhancement module to improve the spatially-varying representation of Mamba. Extensive experiments are conducted on three public datasets for MRI reconstruction under various undersampling patterns. Comprehensive results demonstrate that our method significantly outperforms state-of-the-art methods with lower computational cost. Implementation code will be available in https://***/XiaoMengLiLiLi/DM-Mamba. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep neural networks

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An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution

arXiv

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arXiv 2024年

作者： Duan, Minghong Qu, Linhao Yang, Zhiwei Wang, Manning Zhang, Chenxi Song, Zhijian Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai200032 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China Academy for Engineering and Technology Fudan University Shanghai200433 China

High-quality whole-slide scanning is expensive, complex, and time-consuming, thus limiting the acquisition and utilization of high-resolution histopathology images in daily clinical work. Deep learning-based single-image superresolution (SISR) techniques provide an effective way to solve this problem. However, the existing SISR models applied in histopathology images can only work in fixed integer scaling factors, decreasing their applicability. Though methods based on implicit neural representation (INR) have shown promising results in arbitrary-scale super-resolution (SR) of natural images, applying them directly to histopathology images is inadequate because they have unique fine-grained image textures different from natural images. Thus, we propose an Implicit Self-Texture Enhancement-based dual-branch framework (ISTE) for arbitrary-scale SR of histopathology images to address this challenge. The proposed ISTE contains a feature aggregation branch and a texture learning branch. We employ the feature aggregation branch to enhance the learning of the local details for SR images while utilizing the texture learning branch to enhance the learning of high-frequency texture details. Then, we design a two-stage texture enhancement strategy to fuse the features from the two branches to obtain the SR images. Experiments on publicly available datasets, including TMA, HistoSR, and the TCGA lung cancer datasets, demonstrate that ISTE outperforms existing fixed-scale and arbitrary-scale SR algorithms across various scaling factors. Additionally, extensive experiments have shown that the histopathology images reconstructed by the proposed ISTE are applicable to downstream pathology image analysis tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： image texture

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Intelligent Detection of Hypokalemia Based on 12-Lead ECG Using Two-stream Deep Learning Model 15

Intelligent Detection of Hypokalemia Based on 12-Lead ECG Us...

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15th International Congress on image and Signal Processing, Biomedical Engineering and Informatics, CISP-BMEI 2022

作者： Wang, Yueyi Zhong, Gaoyan Wang, Ya'nan Zhu, Qiang Xie, Jiayong Deng, Xintao Wang, Aiguo Yang, Cuiwei School of Information Science and Technology Fudan University Center for Biomedical Engineering Shanghai China Xinghua City People's Hospital Jiangsu China The Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai China

ISBN: (数字)9781665488877

ISBN: (纸本)9781665488877

Hypokalemia is one of the most common electrolyte disorders in clinic. The detection of hypokalemia mainly depends on the detection of serum potassium concentration. Previous studies have shown that with the decrease of serum potassium ion concentration, ECG will show corresponding characteristics. In this paper, 12-lead ECG is used for intelligent detection of hypokalemia. After six artificial features based on ECG are extracted, a two-stream deep learning model is trained by using these features and 12-lead ECG to detect hypokalemia. The AVC of the two-stream model on the verification set is 0.84, and the AVC on the test set is 0.82. After taking the best working point, on the verification set, the sensitivity is 81.45%, the specificity is 74.21 %, and the recognition accuracy is 77.82%, while on the test set, the sensitivity is 77.54%, the specificity is 74.28%, and the recognition accuracy is 75.91 %. The results show that these time-domain features can significantly improve the recognition accuracy of hypokalemia. © 2022 IEEE.

关键词： Electrocardiograms

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Fusionmlp: A Mlp-Based Unified image Fusion Framework

SSRN

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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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Separate and Conquer: Decoupling Co-occurrence via Decomposition and Representation for Weakly Supervised Semantic Segmentation

Separate and Conquer: Decoupling Co-occurrence via Decomposi...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhiwei Yang Kexue Fu Minghong Duan Linhao Qu Shuo Wang Zhijian Song Academy for Engineering and Technology Fudan University Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shandong Computer Science Center (National Supercomputer Center in Jinan)

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Weakly supervised semantic segmentation (WSSS) with image-level labels aims to achieve segmentation tasks with-out dense annotations. However, attributed to the frequent coupling of co-occurring objects and the limited supervision from image-level labels, the challenging co-occurrence problem is widely present and leads to false activation of objects in WSSS. In this work, we devise a ‘Separate and Conquer’ scheme SeCo to tackle this issue from di-mensions of image space and feature space. In the im-age space, we propose to ‘separate’ the co-occurring ob-jects with image decomposition by subdividing images into patches. Importantly, we assign each patch a category tag from Class Activation Maps (CAMs), which spatially helps remove the co-context bias and guide the subsequent rep-resentation. In the feature space, we propose to ‘conquer’ the false activation by enhancing semantic representation with multi-granularity knowledge contrast. To this end, a dual-teacher-single-student architecture is designed and tag-guided contrast is conducted, which guarantee the cor-rectness of knowledge and further facilitate the discrepancy among co-contexts. We streamline the multi-staged WSSS pipeline end-to-end and tackle this issue without external supervision. Extensive experiments are conducted, validating the efficiency of our method and the superiority over previous single-staged and even multi-staged competitors on PASCAL VOC and MS COCO. Code is available here.

关键词： Couplings computer vision Codes Semantic segmentation Semantics Pipelines Noise reduction

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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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