Lifting-based variational multiclass segmentation algorithm: design, convergence analysis, and implementation with applications in medical imaging
University of Innsbruck（因斯布鲁克大学）；VASCage-Research Centre on Vascular Ageing and Stroke（VASCage血管老化与中风研究中心）；MRC Laboratory of Molecular Biology（英国医学研究委员会分子生物学实验室）；Medical University of Innsbruck（因斯布鲁克医科大学）
Nadja Gruber, Johannes Schwab, Sebastien Court, Elke Gizewski, Markus Haltmeier
We propose, analyze and realize a variational multiclass segmentation scheme that partitions a given image into multiple regions exhibiting specific properties. Our method determines multiple functions that encode the segmentation regions by minimizing an energy functional combining information from different channels. Multichannel image data can be obtained by lifting the image into a higher dimensional feature space using specific multichannel filtering or may already be provided by the imaging modality under consideration, such as an RGB image or multimodal medical data. Experimental results show that the proposed method performs well in various scenarios. In particular, promising results are presented for two medical applications involving classification of brain abscess and tumor growth, respectively. As main theoretical contributions, we prove the existence of global minimizers of the proposed energy functional and show its stability and convergence with respect to noisy inputs. In particular, these results also apply to the special case of binary segmentation, and these results are also novel in this particular situation.
School of Professional Studies（专业研究学院）；Northwestern University（西北大学）
Artificial intelligence (AI) is disrupting the medical field as advances in modern technology allow common household computers to learn anatomical and pathological features that distinguish between healthy and disease with the accuracy of highly specialized, trained physicians. Computer vision AI applications use medical imaging, such as lung chest X-Rays (LCXRs), to facilitate diagnoses by providing second-opinions in addition to a physician's or radiologist's interpretation. Considering the advent of the current Coronavirus disease (COVID-19) pandemic, LCXRs may provide rapid insights to ind…
Many strides have been made in semantic segmentation of multiple classes within an image. This has been largely due to advancements in deep learning and convolutional neural networks (CNNs). Features within a CNN are automatically learned during training, which allows for the abstraction of semantic information within the images. These deep learning models are powerful enough to handle the segmentation of multiple classes without the need for multiple networks. Despite these advancements, few attempts have been made to automatically segment multiple anatomical features within medical imaging d…
We propose performing imbalanced classification by regrouping majority classes into small classes so that we turn the problem into balanced multiclass classification. This new idea is dramatically different from popular loss reweighting and class resampling methods. Our preliminary result on imbalanced medical image classification shows that this natural idea can substantially boost the classification performance as measured by average precision (approximately area-under-the-precision-recall-curve, or AUPRC), which is more appropriate for evaluating imbalanced classification than other metrics…
Deep neural networks have been investigated in learning latent representations of medical images, yet most of the studies limit their approach in a single supervised convolutional neural network (CNN), which usually rely heavily on a large scale annotated dataset for training. To learn image representations with less supervision involved, we propose a deep Siamese CNN (SCNN) architecture that can be trained with only binary image pair information. We evaluated the learned image representations on a task of content-based medical image retrieval using a publicly available multiclass diabetic ret…
Variational multichannel multiclass segmentation using unsupervised lifting with CNNs
University of Innsbruck（因斯布鲁克大学）；VASCage GmbH（VASCage有限责任公司）；MRC-Laboratory of Molecular Biology（英国医学研究委员会分子生物学实验室）；Medical University of Innsbruck（因斯布鲁克医科大学）
Cancer is a leading cause of death in many countries. An early diagnosis of cancer based on biomedical imaging ensures effective treatment and a better prognosis. However, biomedical imaging presents challenges to both clinical institutions and researchers. Physiological anomalies are often characterized by slight abnormalities in individual cells or tissues, making them difficult to detect visually. Traditionally, anomalies are diagnosed by radiologists and pathologists with extensive training. This procedure, however, demands the participation of professionals and incurs a substantial cost. …
INRIA Saclay-Île-de-France（法国国家信息与自动化研究所 萨克莱-法兰西岛分院）；CEA（法国原子能和替代能源委员会）；INRIA Paris - Rocquencourt（法国国家信息与自动化研究所 巴黎-罗克库尔分院）；Inserm（法国国家健康与医学研究院）
Medical images can be used to predict a clinical score coding for the severity of a disease, a pain level or the complexity of a cognitive task. In all these cases, the predicted variable has a natural order. While a standard classifier discards this information, we would like to take it into account in order to improve prediction performance. A standard linear regression does model such information, however the linearity assumption is likely not be satisfied when predicting from pixel intensities in an image. In this paper we address these modeling challenges with a supervised learning proced…
Application of Quantum Convolutional Neural Networks for MRI-Based Brain Tumor Detection and Classification
IPB University（茂物农业大学）；Nguyen Tat Thanh University（阮必成大学）；Princess Nourah bint Abdulrahman University（努拉·宾特·阿卜杜勒拉赫曼公主大学）；King Saud Bin Abdulaziz University for Health Sciences（沙特·本·阿卜杜勒阿齐兹国王健康科学大学）；King Faisal Specialist Hospital & Research Center（费萨尔国王专科医院与研究中心）
Elsevier Science Publishers B.V.（爱思唯尔科学出版公司）；University of Winchester（温彻斯特大学）；Universidad de Granada（格拉纳达大学）；Universidad de Malaga（马拉加大学）；University of Leicester（莱斯特大学）
Respiratory diseases kill million of people each year. Diagnosis of these pathologies is a manual, time-consuming process that has inter and intra-observer variability, delaying diagnosis and treatment. The recent COVID-19 pandemic has demonstrated the need of developing systems to automatize the diagnosis of pneumonia, whilst Convolutional Neural Network (CNNs) have proved to be an excellent option for the automatic classification of medical images. However, given the need of providing a confidence classification in this context it is crucial to quantify the reliability of the model's predict…
University of Cambridge（剑桥大学）；University of Salerno（萨莱诺大学）
Automatic segmentation methods are an important advancement in medical image analysis. Machine learning techniques, and deep neural networks in particular, are the state-of-the-art for most medical image segmentation tasks. Issues with class imbalance pose a significant challenge in medical datasets, with lesions often occupying a considerably smaller volume relative to the background. Loss functions used in the training of deep learning algorithms differ in their robustness to class imbalance, with direct consequences for model convergence. The most commonly used loss functions for segmentati…
Silesian University of Technology（西里西亚理工大学）；Medical University of Silesia（西里西亚医科大学）；Voivodship Specialist Hospital（省专科医院）；Central Clinical Hospital of the Ministry of Internal Affairs and Administration（内务与行政部中央临床医院）；Jagiellonian University Medical College（雅盖隆大学医学院）；Kujawsko-Pomorskie Pulmonology Center（库亚维-波美拉尼亚肺病中心）；Silesian Hospital（西里西亚医院）；Medical University of Warsaw（华沙医科大学）；Nicolaus Copernicus University（尼古拉斯·哥白尼大学）；Medical University of Bialystok（比亚韦斯托克医科大学）；Wroclaw Medical University（弗罗茨瓦夫医科大学）；Czerniakowski Hospital（切尔尼亚科夫斯基医院）；MEGREZ Hospital（MEGREZ医院）；Medical University of Gdansk（格但斯克医科大学）；Yale School of Medicine（耶鲁大学医学院）；The Maria Sklodowska-Curie National Research Institute of Oncology（玛丽·斯克沃多夫斯卡-居里国家肿瘤研究所）
The outbreak of the SARS-CoV-2 pandemic has put healthcare systems worldwide to their limits, resulting in increased waiting time for diagnosis and required medical assistance. With chest radiographs (CXR) being one of the most common COVID-19 diagnosis methods, many artificial intelligence tools for image-based COVID-19 detection have been developed, often trained on a small number of images from COVID-19-positive patients. Thus, the need for high-quality and well-annotated CXR image databases increased. This paper introduces POLCOVID dataset, containing chest X-ray (CXR) images of patients w…
We propose a framework for rotation and translation covariant deep learning using $SE(2)$ group convolutions. The group product of the special Euclidean motion group $SE(2)$ describes how a concatenation of two roto-translations results in a net roto-translation. We encode this geometric structure into convolutional neural networks (CNNs) via $SE(2)$ group convolutional layers, which fit into the standard 2D CNN framework, and which allow to generically deal with rotated input samples without the need for data augmentation. We introduce three layers: a lifting layer which lifts a 2D (vector va…
A Distributed Hybrid Quantum Convolutional Neural Network for Medical Image Classification
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education（教育部智能感知与图像理解重点实验室）；Joint International Research Laboratory of Intelligent Perception and Computation（智能感知与计算联合国际研究实验室）；Collaborative Innovation Center of Quantum Information of Shaanxi Province（陕西省量子信息协同创新中心）；School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）
In-hoc Concept Representations to Regularise Deep Learning in Medical Imaging
The Netherlands Cancer Institute（荷兰癌症研究所）；Utrecht University（乌得勒支大学）；Maastricht University（马斯特里赫特大学）；University of Amsterdam（阿姆斯特丹大学）；Amsterdam UMC（阿姆斯特丹大学医学中心）；UiT The Arctic University of Norway（挪威北莫斯堡大学）
Pyramid Pixel Context Adaption Network for Medical Image Classification with Supervised Contrastive Learning
Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology（南方科技大学可信自主系统研究院）；Southern University of Science and Technology（南方科技大学）；Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Southern University of Science and Technology（南方科技大学广东省类脑智能计算重点实验室）；Singapore Eye Research Institute（新加坡眼科研究所）
Advancing Diagnostic Precision: Leveraging Machine Learning Techniques for Accurate Detection of Covid-19, Pneumonia, and Tuberculosis in Chest X-Ray Images
Deep learning and classical computer vision techniques in medical image analysis: Case studies on brain MRI tissue segmentation, lung CT COPD registration, and skin lesion classification
University of Exeter（埃克塞特大学）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）；Sorbonne Université（索邦大学）
Université Côte d’Azur（蔚蓝海岸大学）；Inria Sophia Antipolis（法国国家信息与自动化研究所索菲亚·安提波利斯中心）；Microsoft Research Cambridge（微软研究院剑桥实验室）
We present an efficient deep learning approach for the challenging task of tumor segmentation in multisequence MR images. In recent years, Convolutional Neural Networks (CNN) have achieved state-of-the-art performances in a large variety of recognition tasks in medical imaging. Because of the considerable computational cost of CNNs, large volumes such as MRI are typically processed by subvolumes, for instance slices (axial, coronal, sagittal) or small 3D patches. In this paper we introduce a CNN-based model which efficiently combines the advantages of the short-range 3D context and the long-ra…
CONNECT Centre, Trinity College Dublin（都柏林圣三一大学CONNECT中心）；University of Sindh（信德大学）；Signy Advanced Technologies（西格尼先进技术公司）；Indian Institute of Technology, Dehli（印度理工学院德里分校）；King Faisal University（费萨尔国王大学）
The current COVID-19 pandemic has motivated the researchers to use artificial intelligence techniques for a potential alternative to reverse transcription-polymerase chain reaction (RT-PCR) due to the limited scale of testing. The chest X-ray (CXR) is one of the alternatives to achieve fast diagnosis but the unavailability of large-scale annotated data makes the clinical implementation of machine learning-based COVID detection difficult. Another issue is the usage of ImageNet pre-trained networks which does not extract reliable feature representations from medical images. In this paper, we pro…
Universidad Politécnica de Madrid（马德里理工大学）；Universidad Autónoma de Madrid（马德里自治大学）；TECNALIA（特克纳利亚研究中心）；Basque Research & Technology Alliance（巴斯克研究与技术联盟）
Over the last few years, convolutional neural networks (CNNs) have dominated the field of computer vision thanks to their ability to extract features and their outstanding performance in classification problems, for example in the automatic analysis of X-rays. Unfortunately, these neural networks are considered black-box algorithms, i.e. it is impossible to understand how the algorithm has achieved the final result. To apply these algorithms in different fields and test how the methodology works, we need to use eXplainable AI techniques. Most of the work in the medical field focuses on binary …
Learned Image resizing with efficient training (LRET) facilitates improved performance of large-scale digital histopathology image classification models
DCT-HistoTransformer: Efficient Lightweight Vision Transformer with DCT Integration for histopathological image analysis
Department of Medical Engineering, Electrical Engineering Department, Sharif University of Technology（谢赫·伊斯兰技术大学医学工程系、电气工程系）；Department of Mathematical and Computer Sciences, Kharazmi University（卡拉齐米大学数学与计算机科学系）
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…
Detection of colon polyps has become a trending topic in the intersecting fields of machine learning and gastrointestinal endoscopy. The focus has mainly been on per-frame classification. More recently, polyp segmentation has gained attention in the medical community. Segmentation has the advantage of being more accurate than per-frame classification or object detection as it can show the affected area in greater detail. For our contribution to the EndoCV 2021 segmentation challenge, we propose two separate approaches. First, a segmentation model named TriUNet composed of three separate UNet m…
Indian Institute of Information Technology Allahabad（阿拉哈巴德印度信息技术学院）
Computer-aided histopathological image analysis for cancer detection is a major research challenge in the medical domain. Automatic detection and classification of nuclei for cancer diagnosis impose a lot of challenges in developing state of the art algorithms due to the heterogeneity of cell nuclei and data set variability. Recently, a multitude of classification algorithms has used complex deep learning models for their dataset. However, most of these methods are rigid and their architectural arrangement suffers from inflexibility and non-interpretability. In this research article, we have p…
Xinjiang University（新疆大学）；Software College, Xinjiang University（新疆大学软件学院）；Network Center, Xinjiang University（新疆大学网络中心）
To better retain the deep features of an image and solve the sparsity problem of the end-to-end segmentation model, we propose a new deep convolutional network model for medical image pixel segmentation, called MC-Net. The core of this network model consists of four parts, namely, an encoder network, a multiple max-pooling integration module, a cross multiscale deconvolution decoder network and a pixel-level classification layer. In the network structure of the encoder, we use multiscale convolution instead of the traditional single-channel convolution. The multiple max-pooling integration mod…
Automatic Labels are as Effective as Manual Labels in Biomedical Images Classification with Deep Learning
Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais)（瑞士西部应用科学与艺术大学信息系统研究所）；University of Padua（帕多瓦大学）；University of Applied Sciences and Arts Dortmund（多特蒙德应用科学与艺术大学）；AGH University of Kraków（克拉科夫AGH科技大学）；SURFsara；Ruggi University Hospital（鲁吉大学医院）；Gravina Hospital Caltagirone ASP（卡尔塔吉罗内格拉维纳医院）；Ontotext；Bulgarian Academy of Sciences（保加利亚科学院）；Radboud University Medical Center（拉德堡德大学医学中心）；University of Geneva（日内瓦大学）
