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Strided Self-Supervised Low-Dose CT Denoising for Lung Nodule Classification

Phenomics 2021年第6期1卷 257-268页

作者： Yiming Lei Junping Zhang Hongming Shan Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science Fudan UniversityShanghai 200433China Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai 201210China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence(Fudan University) Ministry of EducationShanghai 201210China

Lung nodule classification based on low-dose computed tomography(LDCT)images has attracted major attention thanks to the reduced radiation dose and its potential for early diagnosis of lung cancer from LDCT-based lung cancer ***,LDCT images suffer from severe noise,largely influencing the performance of lung nodule *** methods combining denoising and classification tasks typically require the corresponding normal-dose CT(NDCT)images as the supervision for the denoising task,which is impractical in the context of clinical diagnosis using *** jointly train these two tasks in a unified framework without the NDCT images,this paper introduces a novel self-supervised method,termed strided Noise2Neighbors or SN2N,for blind medical image denoising and lung nodule classification,where the supervision is generated from noisy input *** specifically,the proposed SN2N can construct the supervision infor-mation from its neighbors for LDCT denoising,which does not need NDCT images *** proposed SN2N method enables joint training of LDCT denoising and lung nodule classification tasks by using self-supervised loss for denoising and cross-entropy loss for *** experimental results on the Mayo LDCT dataset demonstrate that our SN2N achieves competitive performance compared with the supervised learning methods that have paired NDCT images as ***,our results on the LIDC-IDRI dataset show that the joint training of LDCT denoising and lung nodule classification significantly improves the performance of LDCT-based lung nodule classification.

关键词： Convolutional neural network Medical image classification Self-supervised denoising Low-dose CT

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Self-supervised Learning of Orc-Bert Augmentor for Recognizing Few-Shot Oracle Characters 15th

Self-supervised Learning of Orc-Bert Augmentor for Recognizi...

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15th Asian Conference on computer Vision, ACCV 2020

作者： Han, Wenhui Ren, Xinlin Lin, Hangyu Fu, Yanwei Xue, Xiangyang School of Data Science Computer Science and MOE Frontiers Center for Brain Science Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai China

ISBN: (纸本)9783030695439

This paper studies the recognition of oracle character, the earliest known hieroglyphs in China. Essentially, oracle character recognition suffers from the problem of data limitation and imbalance. Recognizing the oracle characters of extremely limited samples, naturally, should be taken as the few-shot learning task. Different from the standard few-shot learning setting, our model has only access to large-scale unlabeled source Chinese characters and few labeled oracle characters. In such a setting, meta-based or metric-based few-shot methods are failed to be efficiently trained on source unlabeled data;and thus the only possible methodologies are self-supervised learning and data augmentation. Unfortunately, the conventional geometric augmentation always performs the same global transformations to all samples in pixel format, without considering the diversity of each part within a sample. Moreover, to the best of our knowledge, there is no effective self-supervised learning method for few-shot learning. To this end, this paper integrates the idea of self-supervised learning in data augmentation. And we propose a novel data augmentation approach, named Orc-Bert Augmentor pre-trained by self-supervised learning, for few-shot oracle character recognition. Specifically, Orc-Bert Augmentor leverages a self-supervised BERT model pre-trained on large unlabeled Chinese characters datasets to generate sample-wise augmented samples. Given a masked input in vector format, Orc-Bert Augmentor can recover it and then output a pixel format image as augmented data. Different mask proportion brings diverse reconstructed output. Concatenated with Gaussian noise, the model further performs point-wise displacement to improve diversity. Experimentally, we collect two large-scale datasets of oracle characters and other Chinese ancient characters for few-shot oracle character recognition and Orc-Bert Augmentor pre-training. Extensive experiments on few-shot learning demonstrate the effe

关键词： Character recognition

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CORE: Learning Consistent Ordinal REpresentations for Image Ordinal Estimation

arXiv

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arXiv 2023年

作者： Lei, Yiming Li, Zilong Li, Yangyang Zhang, Junping Shan, Hongming Shanghai Key Laboratory of Intelligent Information Processing The School of Computer Science Fudan University Shanghai200433 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Institute of Science and Technology for Brain-Inspired Intelligence MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai200031 China

The goal of image ordinal estimation is to estimate the ordinal label of a given image with a convolutional neural network. Existing methods are mainly based on ordinal regression and particularly focus on modeling the ordinal mapping from the feature representation of the input to the ordinal label space. However, the manifold of the resultant feature representations does not maintain the intrinsic ordinal relations of interest, which hinders the effectiveness of the image ordinal estimation. Therefore, this paper proposes learning intrinsic Consistent Ordinal REpresentations (CORE) from ordinal relations residing in ground-truth labels while encouraging the feature representations to embody the ordinal low-dimensional manifold. First, we develop an ordinal totally ordered set (toset) distribution (OTD), which can (i) model the label embeddings to inherit ordinal information and measure distances between ordered labels of samples in a neighborhood, and (ii) model the feature embeddings to infer numerical magnitude with unknown ordinal information among the features of different samples. Second, through OTD, we convert the feature representations and labels into the same embedding space for better alignment, and then compute the Kullback–Leibler (KL)-divergence between the ordinal labels and feature representations to endow the latent space with consistent ordinal relations. Third, due to the covariate shift from the feature representations to ordinal labels, we optimize the KL-divergence through ordinal prototype-constrained convex programming with dual decomposition;our theoretical analysis shows that we can obtain the optimal solutions via gradient back-propagation. The results show that the proposed CORE is easily implemented and plug-and-play compatible with existing methods without needing to modify network architectures. Extensive experimental results on four different scenarios—facial age estimation, medical disease progression prediction, historical image d

关键词： Convex optimization

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CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization

arXiv

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arXiv 2023年

作者： Gao, Qi Li, Zilong Zhang, Junping Zhang, Yi Shan, Hongming The Institute of Science and Technology for Brain-inspired Intelligence and MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China Shanghai Center for Brain Science and Brain-inspired Technology Shanghai201602 China The Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China School of Cyber Science and Engineering Sichuan University Sichuan Chengdu610065 China

Low-dose computed tomography (CT) images suffer from noise and artifacts due to photon starvation and electronic noise. Recently, some works have attempted to use diffusion models to address the over-smoothness and training instability encountered by previous deep-learning-based denoising models. However, diffusion models suffer from long inference time due to a large number of sampling steps involved. Very recently, cold diffusion model generalizes classical diffusion models and has greater flexibility. Inspired by cold diffusion, this paper presents a novel COntextual eRror-modulated gEneralized Diffusion model for low-dose CT (LDCT) denoising, termed CoreDiff. First, CoreDiff utilizes LDCT images to displace the random Gaussian noise and employs a novel mean-preserving degradation operator to mimic the physical process of CT degradation, significantly reducing sampling steps thanks to the informative LDCT images as the starting point of the sampling process. Second, to alleviate the error accumulation problem caused by the imperfect restoration operator in the sampling process, we propose a novel ContextuaL Error-modulAted Restoration Network (CLEAR-Net), which can leverage contextual information to constrain the sampling process from structural distortion and modulate time step embedding features for better alignment with the input at the next time step. Third, to rapidly generalize the trained model to a new, unseen dose level with as few resources as possible, we devise a one-shot learning framework to make CoreDiff generalize faster and better using only one single LDCT image (un)paired with normal-dose CT (NDCT). Extensive experimental results on four datasets demonstrate that our CoreDiff outperforms competing methods in denoising and generalization performance, with clinically acceptable inference time. Source code is made available at https://***/qgao21/CoreDiff. Copyright © 2023, The Authors. All rights reserved.

关键词： Errors

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DMNER: Biomedical Named Entity Recognition by Detection and Matching

DMNER: Biomedical Named Entity Recognition by Detection and ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Junyi Bian Rongze Jiang Weiqi Zhai Tianyang Huang Xiaodi Huang Hong Zhou Shanfeng Zhu School of Computer Science Fudan University Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China School of Computing Mathematics and Engineering Charles Sturt University New South Wales Australia Atypon Systems LLC UK Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science Shanghai Key Lab of Intelligent Information Processing Zhangjiang Fudan International Innovation Center Fudan University Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Biomedical Named Entity Recognition (NER) is a crucial task in extracting information from biomedical texts. However, the diversity of professional terminology, semantic complexity, and the widespread presence of synonyms pose significant challenges. Traditional methods that rely on sequence labeling training datasets often struggle to handle these complexities. To address this, we introduce a novel framework for BioNER, termed DMNER, which leverages external knowledge and operates in two steps: entity boundary detection and entity category identification through matching. The core of DMNER is its second step, which determines the entity category by retrieving similar entities and their categories from a knowledge dictionary using semantic similarity matching. Our experiments on 10 biomedical datasets demonstrate that DMNER outperforms baselines across these tasks, proving its effectiveness and adaptability. DMNER is versatile and can be applied to various NER tasks, including supervised NER, distantly supervised NER, and NER on multiple datasets with disjoint label sets. The DMNER code is publicly available 1 .

关键词： Training Dictionaries Codes Semantics Named entity recognition Complexity theory Labeling Data mining Bioinformatics

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Prognosis for patients with cognitive motor dissociation identified by brain-computer interface (vol 143, pg 1177, 2020)

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brain 2020年第8期143卷 1177-1189页

作者： Pan, Jiahui Xie, Qiuyou Qin, Pengmin Chen, Yan He, Yanbin Huang, Haiyun Wang, Fei Ni, Xiaoxiao Cichocki, Andrzej Yu, Ronghao Li, Yuanqing Center for Brain-Computer Interfaces and Brain Information Processing South China University of Technology Guangzhou China School of Software South China Normal University Guangzhou China Department of Rehabilitation Medicine Zhujiang Hospital Southern Medical University Guangzhou China Centre for Hyperbaric Oxygen and Neurorehabilitation Guangzhou General Hospital of Guangzhou Military Command Guangzhou China Centre for Studies of Psychological Applications Guangdong Key Laboratory of Mental Health and Cognitive Science School of Psychology South China Normal University Guangzhou China Centre for Hyperbaric Oxygen and Neurorehabilitation Guangzhou General Hospital of Guangzhou Military Command Guangzhou China Centre for Hyperbaric Oxygen and Neurorehabilitation Guangzhou General Hospital of Guangzhou Military Command Guangzhou China Department of Traumatic Brain Injury Rehabilitation and Severe Rehabilitation Guangdong Work Injury Rehabilitation Hospital Guangzhou China Center for Brain-Computer Interfaces and Brain Information Processing South China University of Technology Guangzhou China Center for Brain-Computer Interfaces and Brain Information Processing South China University of Technology Guangzhou China School of Software South China Normal University Guangzhou China Centre for Hyperbaric Oxygen and Neurorehabilitation Guangzhou General Hospital of Guangzhou Military Command Guangzhou China Skolkovo Institute of Science and Technology (Skoltech) Moscow 143026 Russia Nicolaus Copernicus University (UMK) Torun 87-100 Poland Centre for Hyperbaric Oxygen and Neurorehabilitation Guangzhou General Hospital of Guangzhou Military Command Guangzhou China Center for Brain-Computer Interfaces and Brain Information Processing South China University of Technology Guangzhou China Correspondence to: Yuanqing Li PhD Center for Brain-Computer Interfaces and Brain Information Processing South China University of Technology Guangzhou 510640 China E-mail: auyqli@scut.edu.c

Cognitive motor dissociation describes a subset of patients with disorders of consciousness who show neuroimaging evidence of consciousness but no detectable command-following behaviours. Although essential for family counselling, decision-making, and the design of rehabilitation programmes, the prognosis for patients with cognitive motor dissociation remains under-investigated. The current study included 78 patients with disorders of consciousness who showed no detectable command-following behaviours. These patients included 45 patients with unresponsive wakefulness syndrome and 33 patients in a minimally conscious state, as diagnosed using the Coma Recovery Scale-Revised. Each patient underwent an EEG-based brain-computer interface experiment, in which he or she was instructed to perform an item-selection task (i.e. select a photograph or a number from two candidates). Patients who achieved statistically significant brain-computer interface accuracies were identified as cognitive motor dissociation. Two evaluations using the Coma Recovery Scale-Revised, one before the experiment and the other 3 months later, were carried out to measure the patients’ behavioural improvements. Among the 78 patients with disorders of consciousness, our results showed that within the unresponsive wakefulness syndrome patient group, 15 of 18 patients with cognitive motor dissociation (83.33%) regained consciousness, while only five of the other 27 unresponsive wakefulness syndrome patients without significant brain-computer interface accuracies (18.52%) regained consciousness. Furthermore, within the minimally conscious state patient group, 14 of 16 patients with cognitive motor dissociation (87.5%) showed improvements in their Coma Recovery Scale-Revised scores, whereas only four of the other 17 minimally conscious state patients without significant brain-computer interface accuracies (23.53%) had improved Coma Recovery Scale-Revised scores. Our results suggest that patients with cogn

关键词： cognitive motor dissociation brain-computer interface disorders of consciousness prognosis unresponsive wakefulness syndrome

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SAN-Net: Learning Generalization to Unseen Sites for Stroke Lesion Segmentation with Self-Adaptive Normalization

arXiv

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arXiv 2022年

作者： Yu, Weiyi Huang, Zhizhong Zhang, Junping Shan, Hongming Institute of Science and Technology for Brain-Inspired Intelligence MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China Shanghai Key Lab of Intelligent Information Processing The School of Computer Science Fudan University Shanghai200433 China Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai201210 China

There are considerable interests in automatic stroke lesion segmentation on magnetic resonance (MR) images in the medical imaging field, as stroke is an important cerebrovascular disease. Although deep learning-based models have been proposed for this task, generalizing these models to unseen sites is difficult due to not only the large inter-site discrepancy among different scanners, imaging protocols, and populations, but also the variations in stroke lesion shape, size, and location. To tackle this issue, we introduce a self-adaptive normalization network, termed SAN-Net, to achieve adaptive generalization on unseen sites for stroke lesion segmentation. Motivated by traditional z-score normalization and dynamic network, we devise a masked adaptive instance normalization (MAIN) to minimize inter-site discrepancies, which standardizes input MR images from different sites into a site-unrelated style by dynamically learning affine parameters from the input;i.e., MAIN can affinely transform the intensity values. Then, we leverage a gradient reversal layer to force the U-net encoder to learn site-invariant representation with a site classifier, which further improves the model generalization in conjunction with MAIN. Finally, inspired by the "pseudosymmetry" of the human brain, we introduce a simple yet effective data augmentation technique, termed symmetry-inspired data augmentation (SIDA), that can be embedded within SAN-Net to double the sample size while halving memory consumption. Experimental results on the benchmark Anatomical Tracings of Lesions After Stroke (ATLAS) v1.2 dataset, which includes MR images from 9 different sites, demonstrate that under the "leave-one-site-out" setting, the proposed SAN-Net outperforms recently published methods in terms of quantitative metrics and qualitative comparisons. © 2022, CC BY.

关键词： Magnetic resonance

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A theoretical framework for the assessment of water fraction-dependent longitudinal decay rates and magnetisation transfer in membrane lipid phantoms

arXiv

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arXiv 2024年

作者： Neeb, Heiko Schyboll, Felix Shaharabani, Rona Mezer, Aviv A. Shtangel, Oshrat Department of Mathematics Computer Science and Technology University of Applied Sciences Koblenz RheinAhrCampus Remagen Germany Institute for Medical Engineering and Information Processing - MTI Mittelrhein University of Koblenz Germany Edmond and Lily Safra Center for Brain Sciences Hebrew University of Jerusalem Israel

Motivation Phantom systems consisting of liposome suspensions are widely employed to investigate quantitative MRI parameters mimicking cellular membranes. The proper physical understanding of the measurement results, however, requires proper models for liposomes and their interaction with the surrounding water molecules. Molecular dynamics (MD) simulations allow for investigating the basic lipid-water interaction and determining quantitative MR parameters such as R1 = 1/T1. Here, we present an MD-based approach for the theoretical prediction of R1, the dependence of R1 on water concentration and the magnetization exchange between lipids and interacting water layer in lipids and lipid mixtures. Moreover, a new parameter is introduced which quantitatively measures the amount of hydration water based on conventional spoiled gradient echo MR acquisitions. Methods Molecular dynamics simulations were performed to determine the native R1 rates in three lipids and their interacting water pools. Employing a three-pool exchange model between lipid, hydration water and free water, the hydration water fraction, fHW, as a new parameter as well as the magnetization transfer rate between hydration water and lipids, kHW,L, were quantitatively determined, from which the water concentration dependence of R1 was predicted for all liposome systems investigated. Results Both fHW and kHW,L were determined quantitatively from spoiled gradient echo data by taking the MD-determined relaxation rates into account. Liposome systems behaved similarly, apart from PLPC which showed both lower hydration water fraction and lower exchange rate. The extracted parameters accurately predicted the measured water fraction-dependent R1 rates. Conclusion Hydration water fraction and magnetisation transfer between lipids and water can be determined by a combination of spoiled gradient echo acquisitions and MD-derived relaxation rates. The parameters enable a theoretical understanding of MR parameters in lip

关键词： Phantoms

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Meta Ordinal Regression Forest for Medical Image Classification with Ordinal Labels

arXiv

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arXiv 2022年

作者： Lei, Yiming Zhu, Haiping Zhang, Junping Shan, Hongming The Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China The Institute of Science and Technology for Brain-inspired Intelligence MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China The Shanghai Center for Brain Science and Brain-inspired Technology Shanghai200031 China

The performance of medical image classification has been enhanced by deep convolutional neural networks (CNNs), which are typically trained with cross-entropy (CE) loss. However, when the label presents an intrinsic ordinal property in nature, e.g., the development from benign to malignant tumor, CE loss cannot take into account such ordinal information to allow for better generalization. To improve model generalization with ordinal information, we propose a novel meta ordinal regression forest (MORF) method for medical image classification with ordinal labels, which learns the ordinal relationship through the combination of convolutional neural network and differential forest in a meta-learning framework. The merits of the proposed MORF come from the following two components: a tree-wise weighting net (TWW-Net) and a grouped feature selection (GFS) module. First, the TWW-Net assigns each tree in the forest with a specific weight that is mapped from the classification loss of the corresponding tree. Hence, all the trees possess varying weights, which is helpful for alleviating the tree-wise prediction variance. Second, the GFS module enables a dynamic forest rather than a fixed one that was previously used, allowing for random feature perturbation. During training, we alternatively optimize the parameters of the CNN backbone and TWW-Net in the metalearning framework through calculating the Hessian matrix. Experimental results on two medical image classification datasets with ordinal labels, i.e., LIDC-IDRI and Breast Ultrasound Dataset, demonstrate the superior performances of our MORF method over existing state-of-the-art methods. © 2022, CC BY.

关键词： Convolutional neural networks

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Point, Segment and Count: A Generalized Framework for Object Counting

arXiv

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arXiv 2023年

作者： Huang, Zhizhong Dai, Mingliang Zhang, Yi Zhang, Junping Shan, Hongming Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China School of Cyber Science and Engineering Sichuan University Chengdu610065 China Institute of Science and Technology for Brain-inspired Intelligence MOE Frontiers Center for Brain Science Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Shanghai200433 China

Class-agnostic object counting aims to count all objects in an image with respect to example boxes or class names, a.k.a few-shot and zero-shot counting. In this paper, we propose a generalized framework for both few-shot and zero-shot object counting based on detection. Our framework combines the superior advantages of two foundation models without compromising their zero-shot capability: (i) SAM to segment all possible objects as mask proposals, and (ii) CLIP to classify proposals to obtain accurate object counts. However, this strategy meets the obstacles of efficiency overhead and the small crowded objects that cannot be localized and distinguished. To address these issues, our framework, termed PseCo, follows three steps: point, segment, and count. Specifically, we first propose a class-agnostic object localization to provide accurate but least point prompts for SAM, which consequently not only reduces computation costs but also avoids missing small objects. Furthermore, we propose a generalized object classification that leverages CLIP image/text embeddings as the classifier, following a hierarchical knowledge distillation to obtain discriminative classifications among hierarchical mask proposals. Extensive experimental results on FSC-147, COCO, and LVIS demonstrate that PseCo achieves state-of-the-art performance in both few-shot/zero-shot object counting/detection. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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