Article
Biology
Chae Eun Lee, Hyelim Park, Yeong-Gil Shin, Minyoung Chung
Summary: The research introduces an adversarial learning-based semi-supervised medical image segmentation method that effectively embeds local and global features and learns context relations between multiple classes. Experimental results show that the method performs well in both single-class and multi-class segmentation, successfully leveraging unlabeled data to improve network performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Artificial Intelligence
Feng Gao, Minhao Hu, Min-Er Zhong, Shixiang Feng, Xuwei Tian, Xiaochun Meng, Ma-yi-di-li Ni-jia-ti, Zeping Huang, Minyi Lv, Tao Song, Xiaofan Zhang, Xiaoguang Zou, Xiaojian Wu
Summary: This paper proposes a novel weakly- and semi-supervised framework named SOUSA, which aims to learn from a small set of sparse annotated data and a large amount of unlabeled data. Extensive experiments demonstrate the robustness and generalization ability of the proposed method on multiple datasets.
MEDICAL IMAGE ANALYSIS
(2022)
Article
Computer Science, Artificial Intelligence
Razieh Sheikhpour
Summary: Feature selection is widely used in machine learning applications to select relevant features from data sets. Recently, there has been considerable research interest in semi-supervised sparse feature selection based on graph Laplacian, which uses the correlation between features. This paper proposes a spline regression-based framework for semi-supervised sparse feature selection, which uses mixed convex and non-convex t2,p-norm regularization to select relevant features and considers feature correlation. The framework retains the geometry structure of labeled and unlabeled data using local spline regression and encodes the data distribution. A unified iterative algorithm is presented to solve the framework, and its convergence is theoretically and experimentally proved. Experiments on several data sets demonstrate the effectiveness of the framework in selecting the most relevant and discriminative features.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Avgoustinos Vouros, Eleni Vasilaki
Summary: This study addresses the problem of data clustering with unidentified feature quality and limited labelled data. A K-Means variant is proposed to combine unsupervised sparse clustering and semi-supervised methods, which effectively identifies informative features from uninformative ones and achieves high performance on synthetic and real world datasets.
PATTERN RECOGNITION LETTERS
(2021)
Article
Computer Science, Information Systems
Chenglong Zhang, Bingbing Jiang, Zidong Wang, Jie Yang, Yangfeng Lu, Xingyu Wu, Weiguo Sheng
Summary: In this paper, an efficient multi-view feature selection method (EMSFS) is proposed to address the issues in multi-view semi-supervised feature selection. EMSFS combines graph learning, label propagation, and multi-view feature selection within a unified framework. The method can adaptively learn a graph and exploit the similarity structure to enhance the reliability of the graph. It also achieves high computational efficiency.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Zhiyu Shao, Jiatong Bao, Jingwei Li, Hongru Tang
Summary: This article proposes a semi-supervised sparse representation method to predict the subjective haptic cognitive intensity in different haptic perceptual dimensions of texture surfaces. Effective data collection and feature extraction steps are conducted, and the results indicate that the proposed method greatly improves accuracy compared to previous methods. The improved method can be implemented to improve the performance of haptic cognitive models for texture surfaces and inspire research on intelligent cognition and haptic rendering systems.
COGNITIVE COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Carlos Sevilla-Salcedo, Vanessa Gomez-Verdejo, Pablo M. Olmos
Summary: The Bayesian approach of factor analysis, known as SSHIBA, is proposed to better adapt to the heterogeneity and sparsity of data, with functions for feature selection and handling missing values, showing great versatility and interpretability, outperforming most state-of-the-art algorithms in various scenarios.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
Ruiqi Wang, Lei Qi, Yinghuan Shi, Yang Gao
Summary: This paper proposes a semi-supervised domain generalization method (SSDG), where only one source domain is fully annotated and other domains are unlabeled. It addresses the domain gap and the inconsistency between generalization and pseudo-labeling by using joint domain-aware labels and a dual-classifier. The experimental results demonstrate the effectiveness of this method.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Hao Chen, Hongmei Chen, Weiyi Li, Tianrui Li
Summary: This study proposes a semi-supervised feature selection algorithm that combines latent representation learning and sparse graph discriminative model to improve the performance of a learning model. The method can consider both the structure information in data space and feature space, and effectively utilize label information. The feasibility and effectiveness of the proposed method are validated through experiments.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Jingliu Lai, Hongmei Chen, Weiyi Li, Tianrui Li, Jihong Wan
Summary: In this study, a novel semi-supervised feature selection model ASLCGLFS is proposed, which combines label information to extend adaptive graph learning for further improving the quality of the similarity matrix. Additionally, adaptive structure learning is introduced to consider global structure and facilitate feature selection.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Chemistry, Analytical
Hao Wang, Juncai Liu, Changhai Huang, Xuewen Yang, Dasha Hu, Liangyin Chen, Xiaoqing Xing, Yuming Jiang
Summary: In this paper, a semi-supervised instance segmentation model called AFT-Mask is proposed, which improves the performance of the feature transfer module by introducing a migration-optimization module, thereby enhancing the accuracy of segmentation prediction.
Article
Environmental Sciences
Yadang Chen, Chenchen Wei, Duolin Wang, Chuanjun Ji, Baozhu Li
Summary: This work presents a few-shot segmentation of remote sensing images with a self-supervised background learner to boost the generalization capacity for unseen categories to handle this challenge. The methodology is divided into two main modules: a meta learner and a background learner, expanding on the classic metric learning framework by optimizing feature representation.
Article
Computer Science, Artificial Intelligence
Jianjian Yin, Zhichao Zheng, Yulu Pan, Yanhui Gu, Yi Chen
Summary: Semi-supervised semantic segmentation aims to classify pixels using both labeled and unlabeled images. The utilization of unlabeled images is crucial in semi-supervised learning. Existing methods tend to focus on reliable pixels while ignoring unreliable pixels, resulting in information loss. Uneven distribution of pixels per category can also lead to misclassification.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Interdisciplinary Applications
Zejian Chen, Wei Zhuo, Tianfu Wang, Jun Cheng, Wufeng Xue, Dong Ni
Summary: In this study, a semi-supervised representation learning method is proposed to enhance the features in the encoder and decoder. The method outperforms existing methods in the segmentation of medical volumes and sequences, and achieves significant improvement even with few labeled data.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Article
Engineering, Biomedical
Jinhui Zhang, Jian Liu, Siyi Wei, Duanduan Chen, Jiang Xiong, Feng Gao
Summary: In this paper, a novel time-dependent weighted feedback fusion based semi-supervised aortic dissection segmentation framework is proposed to alleviate the burden of doctors' labeling. The proposed method outperforms five existing state-of-the-art semi-supervised segmentation methods on both a type-B AD dataset and a public dataset.
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
(2023)
Article
Computer Science, Artificial Intelligence
Hong Liu, Dong Wei, Donghuan Lu, Xiaoying Tang, Liansheng Wang, Yefeng Zheng
Summary: This study proposes a framework based on hybrid 2D-3D convolutional neural networks for obtaining continuous 3D retinal layer surfaces from OCT volumes. The framework works well with both full and sparse annotations and utilizes alignment displacement vectors and layer segmentation to align the B-scans and segment the layers. Experimental results show that the framework outperforms state-of-the-art 2D deep learning methods in terms of layer segmentation accuracy and cross-B-scan 3D continuity.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Simon Oxenford, Ana Sofia Rios, Barbara Hollunder, Clemens Neudorfer, Alexandre Boutet, Gavin J. B. Elias, Jurgen Germann, Aaron Loh, Wissam Deeb, Bryan Salvato, Leonardo Almeida, Kelly D. Foote, Robert Amaral, Paul B. Rosenberg, David F. Tang-Wai, David A. Wolk, Anna D. Burke, Marwan N. Sabbagh, Stephen Salloway, M. Mallar Chakravarty, Gwenn S. Smith, Constantine G. Lyketsos, Michael S. Okun, William S., Zoltan Mari, Francisco A. Ponce, Andres Lozano, Wolf-Julian Neumann, Bassam Al-Fatly, Andreas Horn
Summary: Spatial normalization is a method to map subject brain images to an average template brain, allowing comparison of brain imaging results. We introduce a novel tool called WarpDrive, which enables manual refinements of image alignment after automated registration. The tool improves accuracy of data representation and aids in understanding patient outcomes.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Ricards Marcinkevics, Patricia Reis Wolfertstetter, Ugne Klimiene, Kieran Chin-Cheong, Alyssia Paschke, Julia Zerres, Markus Denzinger, David Niederberger, Sven Wellmann, Ece Ozkan, Christian Knorr, Julia E. Vogt
Summary: This study presents interpretable machine learning models for predicting the diagnosis, management, and severity of suspected appendicitis using ultrasound images. The proposed models utilize concept bottleneck models (CBM) that facilitate interpretation and intervention by clinicians, without compromising performance or requiring time-consuming image annotation.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Jian-Qing Zheng, Ziyang Wang, Baoru Huang, Ngee Han Lim, Bartlomiej W. Papiez
Summary: This article introduces a new method for medical image registration, which utilizes a separable motion backbone and a residual aligner module to better handle the discontinuous motion of multiple neighboring objects. The proposed method achieves excellent registration results on abdominal CT scans and lung CT scans.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangqiong Wu, Guanghua Tan, Hongxia Luo, Zhilun Chen, Bin Pu, Shengli Li, Kenli Li
Summary: This study develops a user-friendly framework for the automated diagnosis of thyroid nodules in ultrasound videos, simulating the diagnostic workflow of radiologists. By interpreting image characteristics and modeling temporal contextual information, the efficiency and generalizability of the diagnosis can be improved.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Riddhish Bhalodia, Shireen Elhabian, Jadie Adams, Wenzheng Tao, Ladislav Kavan, Ross Whitaker
Summary: This paper introduces DeepSSM, a deep learning-based framework for image-to-shape modeling. By learning the functional mapping from images to low-dimensional shape descriptors, DeepSSM can directly infer statistical representation of anatomy from 3D images. Compared to traditional methods, DeepSSM eliminates the need for heavy manual preprocessing and segmentation, and significantly improves computational time.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Florentin Liebmann, Marco von Atzigen, Dominik Stutz, Julian Wolf, Lukas Zingg, Daniel Suter, Nicola A. Cavalcanti, Laura Leoty, Hooman Esfandiari, Jess G. Snedeker, Martin R. Oswald, Marc Pollefeys, Mazda Farshad, Philipp Furnstahl
Summary: This study presents a marker-less approach for automatic registration and real-time navigation of lumbar spinal fusion surgery using a deep neural network, avoiding radiation exposure and surgical errors. The method was validated on an ex-vivo surgery and a public dataset.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Piyush Tiwary, Kinjawl Bhattacharyya, A. P. Prathosh
Summary: Domain shift refers to the change of distributional characteristics between training and testing datasets, leading to performance drop. For medical image tasks, domain shift can be caused by changes in imaging modalities, devices, and staining mechanisms. Existing approaches based on generative models suffer from training difficulties and lack of diversity. In this paper, the authors propose the use of energy-based models (EBMs) for unpaired image-to-image translation in medical images. The proposed method, called Cycle Consistent Twin EBMs (CCT-EBM), employs a pair of EBMs in the latent space of an Auto-Encoder to ensure translation symmetry and coupling between domains.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Yutong Xie, Jianpeng Zhang, Lingqiao Liu, Hu Wang, Yiwen Ye, Johan Verjans, Yong Xia
Summary: This paper proposes a hybrid pre-training paradigm that combines self-supervised learning and supervised learning to improve the representation quality for medical image segmentation tasks. It introduces a reference task in self-supervised learning and optimizes the model using a gradient matching method. The experimental results demonstrate the effectiveness of this approach on multiple medical image segmentation benchmarks.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Youyi Song, Jing Zou, Kup-Sze Choi, Baiying Lei, Jing Qin
Summary: Cell classification is crucial for intelligent cervical cancer screening, but the variation in cells' appearance and shape poses challenges. A new learning algorithm, worse-case boosting, is proposed to improve classification accuracy for under-represented data. Experimental results demonstrate the effectiveness of this algorithm in two publicly available datasets, achieving a 4% improvement in accuracy.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Sangjoon Park, Eun Sun Lee, Kyung Sook Shin, Jeong Eun Lee, Jong Chul Ye
Summary: The increasing demand for AI systems to monitor human errors and abnormalities in healthcare presents challenges. This study presents a model called Medical X-VL, which is tailored for the medical domain and outperformed current state-of-the-art models in two medical image datasets. The model enables various zero-shot tasks for monitoring AI in the medical domain.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Anna Klimovskaia Susmelj, Berkan Lafci, Firat Ozdemir, Neda Davoudi, Xose Luis Dean-Ben, Fernando Perez-Cruz, Daniel Razansky
Summary: Optoacoustic imaging is a technique that uses optical excitation and ultrasound detection for biological tissue imaging. The quality of the images depends on the extent of tomographic coverage provided by the ultrasound detector arrays. However, full coverage is not always possible due to experimental constraints. The proposed signal domain adaptation network aims to reduce limited-view artifacts in the images.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Srijay Deshpande, Muhammad Dawood, Fayyaz Minhas, Nasir Rajpoot
Summary: In this work, a novel framework called SynCLay is proposed for automated synthesis of histology images based on user-defined cellular layouts. The framework can generate realistic and high-quality histology images with different cellular arrangements, which is helpful for studying the role of cells in the tumor microenvironment. The framework integrates a nuclear segmentation and classification model to refine nuclear structures and generate nuclear masks. Evaluation using quantitative metrics and feedback from pathologists shows that the synthetic images generated by SynCLay have high realism scores and can accurately differentiate between benign and malignant tumors.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Ahmed H. Shahin, An Zhao, Alexander C. Whitehead, Daniel C. Alexander, Joseph Jacob, David Barber
Summary: Survival analysis is a valuable tool in healthcare for predicting the time to specific events. This paper introduces CenTime, a novel approach that directly estimates the time to event. The method performs well with censored data and can be easily integrated with deep learning models. Compared to standard methods, CenTime offers superior performance in predicting event time while maintaining comparable ranking performance.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Bingyuan Liu, Jose Dolz, Adrian Galdran, Riadh Kobbi, Ismail Ben Ayed
Summary: Most segmentation losses, such as CE and Dice, are variants of the Cross-Entropy or Dice losses. This work provides a theoretical analysis that shows a deeper connection between CE and Dice than previously thought. From a constrained-optimization perspective, both CE and Dice decompose into similar ground-truth matching terms and region-size penalty terms. The analysis uncovers hidden region-size biases: Dice has an intrinsic bias towards extremely imbalanced solutions, while CE implicitly encourages the ground-truth region proportions. Based on this analysis, a principled and simple solution is proposed to explicitly control the region-size bias.
MEDICAL IMAGE ANALYSIS
(2024)
