Article
Computer Science, Artificial Intelligence
Zhengeng Yang, Hongshan Yu, Yong He, Wei Sun, Zhi-Hong Mao, Ajmal Mian
Summary: This article focuses on learning representation from unlabeled data for semantic segmentation. A novel self-supervised learning framework is developed by formulating the jigsaw puzzle problem as a patch-wise classification problem and solving it with a fully convolutional network. Significant improvements are achieved on the Cityscapes dataset and competitive performance is demonstrated on the PASCAL VOC2012 dataset with fewer time costs on pretraining.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Lei Bi, Michael Fulham, Jinman Kim
Summary: Segmentation of skin lesions using fully convolutional networks is accurate but limited by insufficient training data. Semi-automatic segmentation methods that combine user-inputs with high-level image features offer a better solution for challenging skin lesions. The proposed hyper-fusion network (HFN) in this study outperformed state-of-the-art methods in accuracy and generalizability.
MEDICAL IMAGE ANALYSIS
(2022)
Article
Environmental Sciences
Guanzhou Chen, Xiaoliang Tan, Beibei Guo, Kun Zhu, Puyun Liao, Tong Wang, Qing Wang, Xiaodong Zhang
Summary: Semantic segmentation is a fundamental task in remote sensing image analysis, and our proposed SDFCNv2 framework shows better performance on remote sensing images compared to the SDFCNv1 framework, increasing the mIoU metric by up to 5.22% while using only about half of the parameters.
Article
Geochemistry & Geophysics
Guanzhou Chen, Chanjuan He, Tong Wang, Kun Zhu, Puyun Liao, Xiaodong Zhang
Summary: This article introduces an efficient unsupervised remote sensing image segmentation method based on superpixel segmentation and fully convolutional networks. The method can rapidly achieve pixel-level image segmentation without requiring manual labels or prior knowledge.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Engineering, Electrical & Electronic
Hai Huang, Chang Liu, Lei Tian, Junsheng Mu, Xiaojun Jing
Summary: The proposed video saliency detection model enhances object contour depiction by introducing a ConvLSTM module, allowing it to learn spatial and temporal information. Through the use of augmentation techniques to expand the dataset, and training and evaluation on widely used datasets, the model demonstrates improved performance in detecting moving objects.
INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS
(2021)
Article
Computer Science, Interdisciplinary Applications
Lei Bi, Michael Fulham, Nan Li, Qiufang Liu, Shaoli Song, David Dagan Feng, Jinman Kim
Summary: A recurrent fusion network (RFN) is proposed in this study to progressively fuse multi-modality image features through multiple recurrent fusion phases, producing consistent segmentation results across different network architectures. The RFN method shows more accurate segmentation compared to existing methods and is generalizable to different datasets.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2021)
Article
Computer Science, Artificial Intelligence
Jun Zhang, Zhiyuan Hua, Kezhou Yan, Kuan Tian, Jianhua Yao, Eryun Liu, Mingxia Liu, Xiao Han
Summary: This paper introduces a weakly-supervised model using joint fully convolutional and graph convolutional networks for automated segmentation of pathology images. By utilizing image-level labels instead of pixel-wise annotations, the segmentation model's performance is improved. Experimental results demonstrate the effectiveness of this method in cancer region segmentation.
MEDICAL IMAGE ANALYSIS
(2021)
Article
Computer Science, Artificial Intelligence
Chengrong Yu, Junjie Hu, Guiyuan Li, Shengqian Zhu, Sen Bai, Zhang Yi
Summary: This paper proposes a novel self-supervised learning (SSL) based approach to explore the properties of computed tomography (CT) image features. By utilizing the spatial distance between CT image pairs to develop a new pretext task, the proposed method achieves superior performance in the segmentation of regions of interest (ROIs) in radiotherapy.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Neurosciences
Michele Svanera, Sergio Benini, Dennis Bontempi, Lars Muckli
Summary: The article introduces an optimized neural network CEREBRUM-7T for automatic segmentation of 7T brain MRI, which can accurately and quickly segment brain MRI images for neuroimaging studies.
HUMAN BRAIN MAPPING
(2021)
Article
Computer Science, Artificial Intelligence
Lei Ding, Hao Tang, Yahui Liu, Yilei Shi, Xiao Xiang Zhu, Lorenzo Bruzzone
Summary: This paper proposes an adversarial shape learning network (ASLNet) to improve the accuracy of building segmentation by modeling the shape patterns of buildings. Experimental results show that the proposed ASLNet achieves significant improvements in both pixel-based accuracy and object-based quality measurements.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2022)
Article
Multidisciplinary Sciences
Nayansi Jha, Taehun Kim, Sungwon Ham, Seung-Hak Baek, Sang-Jin Sung, Yoon-Ji Kim, Namkug Kim
Summary: The aim of this study was to develop an auto-segmentation algorithm for mandibular condyle using the 3D U-Net and determine the optimal dataset size for achieving clinically acceptable accuracy through a stress test. The results showed that increasing the training data improves the segmentation accuracy for mandibular condyle fractures.
SCIENTIFIC REPORTS
(2022)
Article
Computer Science, Artificial Intelligence
Pierre-Henri Conze, Ali Emre Kavur, Emilie Cornec-Le Gall, Naciye Sinem Gezer, Yannick Le Meur, M. Alper Selver, Francois Rousseau
Summary: This study achieved multi-organ segmentation in abdominal CT and MR images using deep learning, showing promising results particularly in liver, kidney, and spleen segmentation and outperforming existing schemes.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2021)
Article
Computer Science, Artificial Intelligence
Omar M. Saad, Wei Chen, Fangxue Zhang, Liuqing Yang, Xu Zhou, Yangkang Chen
Summary: In this paper, a fully convolutional DenseNet method for automatic salt segmentation is proposed, with a squeeze-and-excitation network used as a self-attention mechanism to extract the important information related to the salt signals. The method demonstrates robust performance when applied to new datasets using transfer learning and a small amount of training data.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Geography, Physical
Jorge Andres Chamorro Martinez, Laura Elena Cue La Rosa, Raul Queiroz Feitosa, Ieda Del'Arco Sanches, Patrick Nigri Happ
Summary: This paper introduces convolutional recurrent networks for crop recognition in tropical regions with complex spatiotemporal dynamics, achieving per-date crop classification. Experimental results show that the proposed architectures outperform state-of-the-art methods based on recurrent networks in terms of accuracy and F1 score in tropical regions.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2021)
Article
Engineering, Biomedical
Dali F. D. dos Santos, Paulo R. de Faria, Bruno A. N. Travencolo, Marcelo Z. do Nascimento
Summary: The study proposes a method based on a fully convolutional neural network for localizing and performing refined segmentation of tumor regions in histological whole slide images. Experimental results show that the method achieved good results in different cancer-derived datasets with high accuracy up to 97.6%.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Computer Science, Artificial Intelligence
Anam Nazir, Muhammad Nadeem Cheema, Bin Sheng, Ping Li, Huating Li, Guangtao Xue, Jing Qin, Jinman Kim, David Dagan Feng
Summary: This study proposes an Embedded Clustering Sliced U-Net (ECSU-Net) based on 2D U-Net for automatic vertebra segmentation. By integrating three modules, this method achieves excellent performance in terms of computational efficiency and accuracy.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2022)
Article
Neurosciences
Mingyuan Meng, Lei Bi, Michael Fulham, David Dagan Feng, Jinman Kim
Summary: This study proposes an Appearance Adjustment Network (AAN) to enhance the adaptability of deep learning-based registration methods (DLRs) to appearance variations. By providing appearance transformations and generating anatomy-preserving transformations through an anatomy-constrained loss function, our AAN improves the performance of DLRs. Experimental results show that our AAN outperforms state-of-the-art optimization-based registration methods (ORs) and existing DLRs on three public datasets.
Article
Computer Science, Information Systems
Mingyuan Meng, Bingxin Gu, Lei Bi, Shaoli Song, David Dagan Feng, Jinman Kim
Summary: This study proposes a 3D deep multi-task survival model for advanced NPC, which performs survival prediction and tumor segmentation simultaneously. By introducing a hard-sharing segmentation backbone, interference from non-relevant background information is reduced. In addition, a cascaded survival network is introduced to capture prognostic information beyond the primary tumor, leveraging global tumor information derived from the segmentation backbone. Experimental results with two clinical datasets demonstrate the superior performance of the proposed model compared to traditional radiomics-based survival prediction models and existing deep survival models.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2022)
Article
Computer Science, Interdisciplinary Applications
Xiaohang Fu, Lei Bi, Ashnil Kumar, Michael Fulham, Jinman Kim
Summary: The identification of melanoma can be done through the analysis of clinical and dermoscopy images. Current methods lack the ability to fully utilize information from both modalities and exploit the intercategory relationships in the 7PC. This study proposes a graph-based network with two modules to address these limitations and improves classification performance.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2022)
Article
Biology
Xiaohang Fu, Ellis Patrick, Jean Y. H. Yang, David Dagan Feng, Jinman Kim
Summary: The spatial architecture and phenotypic heterogeneity of tumor cells are associated with cancer prognosis and outcomes. Imaging mass cytometry captures high-dimensional maps of disease-relevant biomarkers at single-cell resolution, which can inform patient-specific prognosis. However, existing methods for survival prediction do not utilize spatial phenotype information at the single-cell level, and there is a lack of end-to-end methods that integrate imaging data with clinical information for improved accuracy. We propose a deep multimodal graph-based network that considers spatial phenotype information and clinical variables to enhance survival prediction, and demonstrate its effectiveness in breast cancer datasets.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Information Systems
Usman Naseem, Matloob Khushi, Jinman Kim
Summary: Pathology visual question answering (PathVQA) aims to answer medical questions using pathology images. Existing methods have limitations in capturing the high and low-level interactions between vision and language features required for VQA. Additionally, these methods lack interpretability in justifying the retrieved answers. To address these limitations, a vision-language transformer called TraP-VQA is introduced, which embeds vision and language features for interpretable PathVQA. Our experiments demonstrate that TraP-VQA outperforms state-of-the-art methods and validate its robustness on medical VQA datasets, along with the capability of the integrated vision-language model. Visualization results explain the reasoning behind the retrieved PathVQA answers.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Automation & Control Systems
Yuyu Guo, Lei Bi, Dongming Wei, Liyun Chen, Zhengbin Zhu, Dagan Feng, Ruiyan Zhang, Qian Wang, Jinman Kim
Summary: In this study, we propose a dense-sparse-dense (DSD) motion estimation framework that utilizes unsupervised 3D landmark detection network and motion reconstruction network to extract sparse landmarks and construct motion field in two stages. The method improves the accuracy of motion estimation and preserves anatomical topology.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Wenxiang Ding, Qiaoqiao Ding, Kewei Chen, Miao Zhang, Li Lv, David Dagan Feng, Lei Bi, Jinman Kim, Qiu Huang
Summary: Dynamic PET imaging provides more comprehensive physiological information than conventional static PET imaging. The proposed modified Logan reference plot model and self-supervised convolutional neural network improve noise performance and accurately estimate the distribution volume ratio in dynamic PET with a shortened scanning protocol. The method has the potential to add clinical value by providing both DVR and SUV simultaneously.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Article
Computer Science, Cybernetics
Usman Naseem, Matloob Khushi, Jinman Kim, Adam G. Dunn
Summary: People on social media using disease and symptom words to discuss their health can introduce biases in data-driven public health applications. This study presents a new dataset called RHMD, which consists of 10,015 manually annotated Reddit posts. The dataset is labeled with four categories and provides a comprehensive performance analysis of baseline methods. The release of this dataset is expected to facilitate the development of new methods for detecting health mentions in user-generated text.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Review
Automation & Control Systems
Wei-Chien Wang, Euijoon Ahn, Dagan Feng, Jinman Kim
Summary: Over the last decade, supervised deep learning has made significant progress in computer vision tasks using manually annotated big data. However, the limited availability of high-quality annotated medical imaging data hinders the application of deep learning in medical image analysis. A potential solution is the use of self-supervised learning (SSL), particularly contrastive SSL, which has shown promise in rivaling or surpassing supervised learning. This review examines state-of-the-art contrastive SSL algorithms originally designed for natural images, explores their adaptations for medical images, and discusses recent advances, current limitations, and future directions in applying contrastive SSL in the medical domain.
MACHINE INTELLIGENCE RESEARCH
(2023)
Proceedings Paper
Imaging Science & Photographic Technology
Mingyuan Meng, Lei Bi, Dagan Feng, Jinman Kim
Summary: In this study, a Non-Iterative Coarse-to-finE registration Network (NICE-Net) is proposed for deformable image registration. By using a Single-pass Deep Cumulative Learning (SDCL) decoder and a Selectively-propagated Feature Learning (SFL) encoder, NICE-Net outperforms state-of-the-art iterative deep registration methods without increasing the runtime.
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT VI
(2022)
Article
Computer Science, Cybernetics
Usman Naseem, Matloob Khushi, Jinman Kim, Adam G. Dunn
Summary: The article introduces a hybrid text representation method for explaining suicide risk identification on social media. The method achieves excellent results on a public suicide dataset and demonstrates advantages in clinical and public health practice.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Ahmad Karambakhsh, Bin Sheng, Ping Li, Huating Li, Jinman Kim, Younhyun Jung, C. L. Philip Chen
Summary: The article introduces a novel solution for 3-D object recognition from volumetric data by combining three compact CNN models, low-cost SparseNet, and feature representation technique. By estimating extra geometrical information, an optimized network is achieved and improves the recognition results.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)