Segmentation of medical images is an essential part in the process of diagnostics. Physicians require an automatic, robust and valid results. Hidden Markov Random Fields (HMRF) provide powerful model. This latter models the segmentation problem as the minimization of an energy function. Cuckoo search (CS) algorithm is one of the recent nature-inspired meta-heuristic algorithms. It has shown its efficiency in many engineering optimization problems. In this paper, we use three cuckoo search algorithm to achieve medical image segmentation.
École nationale Supérieure en Informatique（国立高等计算机学院）
In this paper, we present new image segmentation methods based on hidden Markov random fields (HMRFs) and cuckoo search (CS) variants. HMRFs model the segmentation problem as a minimization of an energy function. CS algorithm is one of the recent powerful optimization techniques. Therefore, five variants of the CS algorithm are used to compute a solution. Through tests, we conduct a study to choose the CS variant with parameters that give good results (execution time and quality of segmentation). CS variants are evaluated and compared with non-destructive testing (NDT) images using a misclassi…
Medical image segmentation has been widely recognized as a pivot procedure for clinical diagnosis, analysis, and treatment planning. However, the laborious and expensive annotation process lags down the speed of further advances. Contrastive learning-based weight pre-training provides an alternative by leveraging unlabeled data to learn a good representation. In this paper, we investigate how contrastive learning benefits the general supervised medical segmentation tasks. To this end, patch-dragsaw contrastive regularization (PDCR) is proposed to perform patch-level tugging and repulsing with …
Coordinated Science Laboratory, UIUC（伊利诺伊大学厄巴纳-香槟分校协同科学实验室）；VinUni-Illinois Smart Health Center & CECS, VinUniversity（VinUniversity温大-伊利诺伊智能健康中心与计算机工程与计算机科学学院）；VinUniversity（温大）；Vingroup Big Data Institute（Vingroup大数据研究所）
The work discusses the use of machine learning algorithms for anomaly detection in medical image analysis and how the performance of these algorithms depends on the number of annotators and the quality of labels. To address the issue of subjectivity in labeling with a single annotator, we introduce a simple and effective approach that aggregates annotations from multiple annotators with varying levels of expertise. We then aim to improve the efficiency of predictive models in abnormal detection tasks by estimating hidden labels from multiple annotations and using a re-weighted loss function to…
Ecole nationale Supérieure en Informatique（国立高等信息学院）；Université de Bourgogne（勃艮第大学）
Image segmentation is the process of partitioning the image into significant regions easier to analyze. Nowadays, segmentation has become a necessity in many practical medical imaging methods as locating tumors and diseases. Hidden Markov Random Field model is one of several techniques used in image segmentation. It provides an elegant way to model the segmentation process. This modeling leads to the minimization of an objective function. Conjugate Gradient algorithm (CG) is one of the best known optimization techniques. This paper proposes the use of the Conjugate Gradient algorithm (CG) for …
LEAF: Latent Diffusion with Efficient Encoder Distillation for Aligned Features in Medical Image Segmentation
School of Computer Science and Engineering, Sun Yat-sen University, China（计算机科学与工程学院，中山大学）；Department of Biomedical Engineering, Johns Hopkins University, United States（生物医学工程系，约翰霍普金斯大学）
Med-DisSeg: Dispersion-Driven Representation Learning for Fine-Grained Medical Image Segmentation
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Guangdong Key Laboratory of Big Data Analysis and Processing（广东省大数据分析与处理重点实验室）
EPL: Evidential Prototype Learning for Semi-supervised Medical Image Segmentation
Key Laboratory of High Confidence Software Technologies (Peking University)（高可信软件技术重点实验室（北京大学））；School of Computer Science, Peking University（北京大学计算机学院）
Deep learning has dramatically improved the performance in many application areas such as image classification, object detection, speech recognition, drug discovery and etc since 2012. Where deep learning algorithms promise to discover the intricate hidden information inside the data by leveraging the large dataset, advanced model and computing power. Although deep learning techniques show medical expert level performance in a lot of medical applications, but some of the applications are still not explored or under explored due to the variation of the species. In this work, we studied the brig…
Samsung Electronics（三星电子）；Seoul National University（首尔国立大学）；Soongsil University（崇实大学）
Semi-supervised learning for medical image segmentation is an important area of research for alleviating the huge cost associated with the construction of reliable large-scale annotations in the medical domain. Recent semi-supervised approaches have demonstrated promising results by employing consistency regularization, pseudo-labeling techniques, and adversarial learning. These methods primarily attempt to learn the distribution of labeled and unlabeled data by enforcing consistency in the predictions or embedding context. However, previous approaches have focused only on local discrepancy mi…
SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation
Shanghai Jiao Tong University（上海交通大学）；University of Washington（华盛顿大学）；The Johns Hopkins University（约翰斯·霍普金斯大学）；University of Michigan, Ann Arbor（密歇根大学安娜堡分校）；University of California, Santa Cruz（加利福尼亚大学圣克鲁兹分校）；Tsinghua University（清华大学）；Stanford University（斯坦福大学）
AI Alignment in Medical Imaging: Unveiling Hidden Biases Through Counterfactual Analysis
School of Computation, Information and Technology, Technical University of Munich（技术大学慕尼黑计算、信息与技术学院）；Department of Computer Science, University of Oxford（牛津大学计算机科学系）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）；Helmholtz AI, Munich, Germany（海德堡人工智能研究所，德国慕尼黑）
CLAIM - Charité Lab for AI in Medicine, Charité Universitätsmedizin Berlin（柏林夏里特医学院人工智能医学实验室）；Research Studio Data Science, Research Studios Austria（奥地利研究工作室数据科学研究工作室）；Centre for Stroke Research Berlin, Charité Universitätsmedizin Berlin（柏林夏里特医学院柏林卒中研究中心）；Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences（马克斯·普朗克人类认知与脑科学研究所神经病学系）；School of Computing and Digital Technology, Faculty of Computing, Engineering and the Built Environment, Birmingham City University（伯明翰城市大学计算、工程与建筑环境学院计算与数字技术系）；Mind, Brain, Body Institute, Berlin School of Mind and Brain, Humboldt-Universität Berlin（柏林洪堡大学柏林心智与脑学院心智、脑与身体研究所）
Average Hausdorff Distance (AVD) is a widely used performance measure to calculate the distance between two point sets. In medical image segmentation, AVD is used to compare ground truth images with segmentation results allowing their ranking. We identified, however, a ranking bias of AVD making it less suitable for segmentation ranking. To mitigate this bias, we present a modified calculation of AVD that we have coined balanced AVD (bAVD). To simulate segmentations for ranking, we manually created non-overlapping segmentation errors common in cerebral vessel segmentation as our use-case. Addi…
Zhejiang University（浙江大学）；University of Notre Dame（圣母大学）
In clinical practice, medical image interpretation often involves multi-labeled classification, since the affected parts of a patient tend to present multiple symptoms or comorbidities. Recently, deep learning based frameworks have attained expert-level performance on medical image interpretation, which can be attributed partially to large amounts of accurate annotations. However, manually annotating massive amounts of medical images is impractical, while automatic annotation is fast but imprecise (possibly introducing corrupted labels). In this work, we propose a new regularization approach, …
Technical University of Munich（慕尼黑工业大学）；Siemens AG（西门子股份公司）
Diagnosing diseases such as leukemia or anemia requires reliable counts of blood cells. Hematologists usually label and count microscopy images of blood cells manually. In many cases, however, cells in different maturity states are difficult to distinguish, and in combination with image noise and subjectivity, humans are prone to make labeling mistakes. This results in labels that are often not reproducible, which can directly affect the diagnoses. We introduce TIMELY, a probabilistic model that combines pseudotime inference methods with inhomogeneous hidden Markov trees, which addresses this …
Image segmentation has proved its importance and plays an important role in various domains such as health systems and satellite-oriented military applications. In this context, accuracy, image quality, and execution time deem to be the major issues to always consider. Although many techniques have been applied, and their experimental results have shown appealing achievements for 2D images in real-time environments, however, there is a lack of works about 3D image segmentation despite its importance in improving segmentation accuracy. Specifically, HMM was used in this domain. However, it suff…
University of Electronic Science and Technology of China（电子科技大学）；Institute of Electronic and Information Engineering of UESTC in Guangdong（电子科技大学广东电子信息工程研究院）；National Natural Science Foundation of China（国家自然科学基金委员会）
Internet of Medical Things (IoMT) can connect many medical imaging equipments to the medical information network to facilitate the process of diagnosing and treating for doctors. As medical image contains sensitive information, it is of importance yet very challenging to safeguard the privacy or security of the patient. In this work, a deep learning based encryption and decryption network (DeepEDN) is proposed to fulfill the process of encrypting and decrypting the medical image. Specifically, in DeepEDN, the Cycle-Generative Adversarial Network (Cycle-GAN) is employed as the main learning net…
The Australian Institute for Machine Learning（澳大利亚机器学习研究所）；The University of Adelaide（阿德莱德大学）；The Royal Adelaide Hospital（皇家阿德莱德医院）
Rationale and Objectives: Medical artificial intelligence systems are dependent on well characterised large scale datasets. Recently released public datasets have been of great interest to the field, but pose specific challenges due to the disconnect they cause between data generation and data usage, potentially limiting the utility of these datasets. Materials and Methods: We visually explore two large public datasets, to determine how accurate the provided labels are and whether other subtle problems exist. The ChestXray14 dataset contains 112,120 frontal chest films, and the MURA dataset co…
Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）
Deep convolutional neural networks (CNNs) have been widely used for medical image segmentation. In most studies, only the output layer is exploited to compute the final segmentation results and the hidden representations of the deep learned features have not been well understood. In this paper, we propose a prototype segmentation (ProtoSeg) method to compute a binary segmentation map based on deep features. We measure the segmentation abilities of the features by computing the Dice between the feature segmentation map and ground-truth, named as the segmentation ability score (SA score for shor…
Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局信息通信研究所）；National University of Singapore（新加坡国立大学）
Segmentation is a prerequisite yet challenging task for medical image analysis. In this paper, we introduce a novel deeply supervised active learning approach for finger bones segmentation. The proposed architecture is fine-tuned in an iterative and incremental learning manner. In each step, the deep supervision mechanism guides the learning process of hidden layers and selects samples to be labeled. Extensive experiments demonstrated that our method achieves competitive segmentation results using less labeled samples as compared with full annotation.
Real-time detection of COVID-19 using radiological images has gained priority due to the increasing demand for fast diagnosis of COVID-19 cases. This paper introduces a novel two-phase approach for classifying chest X-ray images. Deep Learning (DL) methods fail to cover these aspects since training and fine-tuning the model's parameters consume much time. In this approach, the first phase comes to train a deep CNN working as a feature extractor, and the second phase comes to use Extreme Learning Machines (ELMs) for real-time detection. The main drawback of ELMs is to meet the need of a large n…
A review of handcrafted and deep radiomics in neurological diseases: transitioning from oncology to clinical neuroimaging
Maastricht University（马斯特里赫特大学）；University of Liege（列日大学）；Maastricht University Medical Centre（马斯特里赫特大学医学中心）；University of Hasselt（哈塞尔特大学）；CHU Liège（列日大学医院中心）；GROW—School for Oncology（GROW肿瘤学院）；Biomedical Research Institute (BIOMED)（生物医学研究所（BIOMED））；GIGA Cyclotrone Research Center（GIGA回旋加速器研究中心）；GIGA In Silico Medicine（GIGA计算机模拟医学中心）；Department of Precision Medicine（精准医学系）；Department of Radiology and Nuclear Medicine（放射科与核医学科）；Neurology Department（神经内科）
