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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Tao, Guihua Li, Haojiang Liu, Lizhi Cai, Hongmin South China University of Technology School of Computer Science and Engineering Guangzhou China Sun Yat-sen University Cancer Center Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Collaborative Innovation Center for Cancer Medicine Guangzhou China Brain and Affective Cognitive Research Center Pazhou Lab Guangzhou China

ISBN: (纸本)9781665401265

Accurate and reliable segmentation of nasopharyngeal carcinoma (NPC) in magnetic resonance image (MRI) is important for treatment planning and follow-up evaluation. However, it is still challenging because NPC is infiltrative with a vague border and has a tiny volume with varying sizes and shapes. The above problems easily cause the NPC tumors' features to submerge in the process of feature extraction. Standard segmentation methods do not cope with the 'feature submergence' and performed unsatisfactorily in NPC segmentation. To address the problem, a dual-supervised method equipped with the detection-and-excitation module (DEM) was proposed. DEM strengthens the region of interest (ROI) and weakens complex and immense background through incorporating detection feature maps (outline maps) with intermediated segmentation features. To be specific, the DEM explored the outline of the ROI and output a detection feature map by using a supervised layer guided by a tailored loss function. Then, the DEM uses the results to recalibrates the intermediated segmentation. Finally, a neural network equipped with the proposed DEM was elaborately designed to achieve accurate segmentation. We applied the proposed network termed as DENet on a real nasopharyngeal carcinoma dataset to realize automatic NPC tumors segmentation in multi-modality MR images. The proposed DEM enables the neural network to segment NPC tumors within the immense and complex background. Experimental results have demonstrated the effectiveness of the proposed method by comparing it with benchmark models. © 2021 IEEE.

关键词： Tumors

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AN ADAPTIVE POLICY TO EMPLOY SHARPNESS-AWARE MINIMIZATION

arXiv

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arXiv 2023年

作者： Jiang, Weisen Yang, Hansi Zhang, Yu Kwok, James Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Peng Cheng Laboratory China

Sharpness-aware minimization (SAM), which searches for flat minima by min-max optimization, has been shown to be useful in improving model generalization. However, since each SAM update requires computing two gradients, its computational cost and training time are both doubled compared to standard empirical risk minimization (ERM). Recent state-of-the-arts reduce the fraction of SAM updates and thus accelerate SAM by switching between SAM and ERM updates randomly or periodically. In this paper, we design an adaptive policy to employ SAM based on the loss landscape geometry. Two efficient algorithms, AE-SAM and AE-LookSAM, are proposed. We theoretically show that AE-SAM has the same convergence rate as SAM. Experimental results on various datasets and architectures demonstrate the efficiency and effectiveness of the adaptive policy. © 2023, CC BY.

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A Hybrid Evolutionary Algorithm for Reliable Facility Location Problem

arXiv

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arXiv 2020年

作者： Zhang, Han Liu, Jialin Yao, Xin Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Secience and Engineering Southern University of Science and Technology Shenzhen518055 China

The reliable facility location problem (RFLP) is an important research topic of operational research and plays a vital role in the decision-making and management of modern supply chain and logistics. Through solving RFLP, the decision-maker can obtain reliable location decisions under the risk of facilities’ disruptions or failures. In this paper, we propose a novel model for the RFLP. Instead of assuming allocating a fixed number of facilities to each customer as in the existing works, we set the number of allocated facilities as an independent variable in our proposed model, which makes our model more close to the scenarios in real life but more difficult to be solved by traditional methods. To handle it, we propose EAMLS, a hybrid evolutionary algorithm, which combines a memorable local search (MLS) method and an evolutionary algorithm (EA). Additionally, a novel metric called l3-value is proposed to assist the analysis of the algorithm’s convergence speed and exam the process of evolution. The experimental results show the effectiveness and superior performance of our EAMLS, compared to a CPLEX solver and a Genetic Algorithm (GA), on large-scale problems. Copyright © 2020, The Authors. All rights reserved.

关键词： Integer programming

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CORE: Learning Consistent Ordinal REpresentations for Image Ordinal Estimation

arXiv

引用

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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Reler: Relearning Controversial Regions to Accurately Segment Nasopharyngeal Carcinoma

Reler: Relearning Controversial Regions to Accurately Segmen...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Tao, Guihua Li, Haojiang Lu, Dandan Ling, Ziqin Liu, Lizhi Cai, Hongmin South China University of Technology School of Computer Science and Engineering Guangzhou China Sun Yat-sen University Cancer Center Sun Yat-sen University State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Guangzhou China Pazhou Lab Brain and Affective Cognitive Research Center Guangzhou China

ISBN: (纸本)9781665468190

Accurate nasopharyngeal carcinoma (NPC) segmentation is significant in preventing local recurrence and improving patients' survival rates. However, existing deep learning-based methods often yield unsatisfactory segmentation results, especially in fine-grained detail. Because NPC is a tiny and infiltrative tumor with a huge background, traditional deep neural networks tend to be dominated by salient information, thus missing the fine-grained details of NPC. To achieve accurate NPC segmentation, a relearning controversial regions method (Reler) is proposed. It consists of three modules, including the controversial features generator (CFG), controversial features finding module (CFF), and controversial regions arbitration module (CRA). First, CFG constructs global and local feature extractors to generate two types of different features. Then, CFF finds the controversial features and corresponding regions by comparing the global and local features' estimates of the segmentation results of the same input regions. Next, the CRA focuses on controversial features, relearns new features, and produces new segmentation results through a proposed Transformer-based self-attention network. Finally, the uncontroversial segmentation results from CFF and CRA are combined as the final segmentation results. Extensive experiments are conducted on a large NPC dataset containing 6342 images from 596 patients. The experimental results show that the proposed method Reler is effective and superior to the nine state-of-the-art methods. © 2022 IEEE.

关键词： Deep neural networks

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Extraction Method for Constructive Proposals based on Online Comments

Extraction Method for Constructive Proposals based on Online...

引用

IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Weipeng Cen Zhigang Gao Ruichao Xu Bo Wu Leilei Zheng Wei Zhao Lei Xiao Xuanzhang He College of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province Hangzhou China School of Computer Science Tokyo University of Technology Hachioji Japan

ISBN: (纸本)9781665421751

In the flipped classroom teaching, it transfers the traditional teaching process from in-class to out-of-class, which is transforming the traditional education mode. In order to make good use of students' feedback data, and improve the quality of the online course and the effect of class teaching, this paper proposes an Extraction Method for constructive Proposals based on online Comments (EMPC). First, EMPC obtains the target words of course comments through BERT (Bidirectional Encoder Representations from Transformers) and the target filter algorithm, then it inputs target words into Bi-LSTM (Bi-directional Long Short-Term Memory) module with attention mechanism to obtain the opinion words. After all the target-opinion pairs are obtained, the algorithm based on k-means is used to extract constructive proposals. We conducted experiments by using MOOC (Massive Open Online Course) courses comment data, the experimental results show that EMPC achieves a high accuracy in both target word extraction and opinion word extraction, and it can get the proposals mentioned by students most frequently in order to improve the flipped classroom teaching.

关键词： computer aided instruction Electronic learning Education Bit error rate Memory modules Filtering algorithms Transformers

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Tumor Micro-Environment Interactions Guided Graph Learning for Survival Analysis of Human Cancers from Whole-Slide Pathological Images

Tumor Micro-Environment Interactions Guided Graph Learning f...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Wei Shao YangYang Shi Daoqiang Zhang JunJie Zhou Peng Wan Ministry of Education College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Key Laboratory of Brain-Machine Intelligence Technology Nanjing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The recent advance of deep learning technology brings the possibility of assisting the pathologist to predict the patients' survival from whole-slide pathological images (WSIs). However, most of the prevalent methods only worked on the sampled patches in specifically or randomly selected tumor areas of WSIs, which has very limited capability to capture the complex interactions between tumor and its surrounding micro-environment components. As a matter of fact, tumor is supported and nurtured in the heterogeneous tumor micro-environment(TME), and the detailed analysis of TME and their correlation with tumors are important to in-depth analyze the mechanism of cancer development. In this paper, we considered the spatial interactions among tumor and its two major TME components (i.e., lymphocytes and stromal fibrosis) and presented a Tumor Micro-environment Interactions Guided Graph Learning (TMEGL) algorithm for the prognosis prediction of human cancers. Specifically, we firstly selected different types of patches as nodes to build graph for each WSI. Then, a novel TME neighborhood organization guided graph embedding algorithm was proposed to learn node representations that can preserve their topological structure information. Finally, a Gated Graph Attention Network is applied to capture the survival-associated intersections among tumor and different TME components for clinical outcome prediction. We tested TMEGL on three cancer cohorts derived from The Cancer Genome Atlas (TCGA), and the experimental results indicated that TMEGL not only outperforms the existing WSI-based survival analysis models, but also has good explainable ability for survival prediction.

关键词： Pathology Analytical models Organizations Predictive models Semisupervised learning Prediction algorithms Pattern recognition

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Data Augmentation-Driven Constantly Corrected Mean Teacher for Semi-Supervised 3D MRI Segmentation

SSRN

引用

SSRN 2023年

作者： Li, Ning Pan, Yudong Qiu, Wei Xiong, Lianjin Zhang, Yangsong Wang, Yaobin School of Computer Science and Technology Laboratory for Brain Science and Medical Artificial Intelligence Southwest University of Science and Technology Mianyang621010 China NHC Key Laboratory of Nuclear Technology Medical Transformation Mianyang Central Hospital Mianyang621000 China

Background and Objective: Semi-supervised learning has garnered significant attention in the realm of medical image segmentation for its ability to mitigate reliance on annotated data. The mean teacher model and its variants, as important methods in semi-supervised learning, have demonstrated promising performance in MR image segmentation. However, the teacher model’s superior performance, achieved through exponential moving average (EMA), is limited by the unreliability of unlabeled image, resulting in potentially unreliable ***: In this paper, we propose a framework to correct the teacher model with reliable expert-annotated data while preserving the advantages of EMA. To avoid the tight coupling that results from EMA, we leverage data augmentations to provide two distinct perspectives for the teacher and student models. The teacher model adopts weak data augmentation to provide supervision for the student model and corrects itself with real annotations, while the student uses strong data augmentation to avoid overfitting on noise information. In addition, double softmax helps the model resist noise and continue learning meaningful information from the images, which is a key component in our ***: Extensive experiments show that the proposed method exhibits competitive performance on two public datasets, including the Left Atrium segmentation MR dataset (LA) and the brain Tumor Segmentation MR dataset (BraTS2019). On the BraTs2019 dataset, the proposed method achieved 1.02% and 1.92% improvement on 5% and 10% labeled data, respectively, compared to the best baseline method on this dataset. It is noteworthy that we achieved a dice of 91.02% using only 20% labeled data for LA, which is close to the dice of 91.14% obtained by the supervised approach using 100% labeled ***: This paper proposes a novel semi-supervised segmentation framework that shows promising performance even with limited number of labeled data. Experimental resul

关键词： Image segmentation

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Hydrogel wound dressings containing bioactive compounds originated from traditional Chinese herbs: A review

引用

Smart Materials in Medicine 2024年第1期5卷 153-165页

作者： Yang, Dan Chen, Hailan Wei, Hua Liu, An Wei, Dai-Xu Chen, Jing Institute of Medical Sciences The Second Hospital and Shandong University Center for Orthopaedics Cheeloo College of Medicine Shandong University Jinan250033 China Faculty of Animal Science and Technology Guangxi University Guangxi Nanning530004 China Key Laboratory of Colloid and Interface Chemistry Ministry of Education School of Chemistry and Chemical Engineering Shandong University Jinan250100 China Department of Orthopedics The Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou310002 China Zigong Affiliated Hospital of Southwest Medical University Zigong Psychiatric Research Center Zigong Institute of Brain Science Zigong Sichuan Province 643002 China Key Laboratory of Resource Biology and Biotechnology in Western China Ministry of Education School of Medicine Faculty of Life Science & Medicine Northwest University Xi'an710069 China

Various factors can cause skin defects, resulting in the loss of physiological functions and even death due to severe concurrent infection. Dressings are often clinically used to fully cover the wounds to improve healing. Hydrogel wound dressings can be loaded with therapeutic compounds (e.g., curcumin) within their three-dimensional networks to enable the in situ delivery of compounds at skin defects for wound healing. In recent decades, natural herbal active components have gradually gained worldwide recognition owing to their safe and diverse therapeutic effects, and an increasing number of bioactive components can be loaded into hydrogels or directly act as hydrogel matrices to enhance safety and achieve the desired therapeutic effects. In this review, twelve bioactive compounds from natural Chinese herbs that can promote wound healing and their mechanism of action are summarized, and the latest research progress in the use of Chinese herbal hydrogels for wound treatment is reviewed. © 2023 The Authors

关键词： Hydrogels

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HV-Net: Hypervolume Approximation based on DeepSets

arXiv

引用

arXiv 2022年

作者： Shang, Ke Chen, Weiyu Liao, Weiduo Ishibuchi, Hisao Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

In this letter, we propose HV-Net, a new method for hypervolume approximation in evolutionary multi-objective optimization. The basic idea of HV-Net is to use DeepSets, a deep neural network with permutation invariant property, to approximate the hypervolume of a non-dominated solution set. The input of HV-Net is a non-dominated solution set in the objective space, and the output is an approximated hypervolume value of this solution set. The performance of HV-Net is evaluated through computational experiments by comparing it with two commonly-used hypervolume approximation methods (i.e., point-based method and line-based method). Our experimental results show that HV-Net outperforms the other two methods in terms of both the approximation error and the runtime, which shows the potential of using deep learning technique for hypervolume approximation. © 2022, CC BY.

关键词： Deep neural networks

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