Automatic lesion segmentation in dermoscopy images is an essential step for computer-aided diagnosis of melanoma. The dermoscopy images exhibits rotational and reflectional symmetry, however, this geometric property has not been encoded in the state-of-the-art convolutional neural networks based skin lesion segmentation methods. In this paper, we present a deeply supervised rotation equivariant network for skin lesion segmentation by extending the recent group rotation equivariant network~\cite{cohen2016group}. Specifically, we propose the G-upsampling and G-projection operations to adapt the rotation equivariant classification network for our skin lesion segmentation problem. To further increase the performance, we integrate the deep supervision scheme into our proposed rotation equivariant segmentation architecture. The whole framework is equivariant to input transformations, including rotation and reflection, which improves the network efficiency and thus contributes to the segmentation performance. We extensively evaluate our method on the ISIC 2017 skin lesion challenge dataset. The experimental results show that our rotation equivariant networks consistently excel the regular counterparts with the same model complexity under different experimental settings. Our best model achieves 77.23\%(JA) on the test dataset, outperforming the state-of-the-art challenging methods and further demonstrating the effectiveness of our proposed deeply supervised rotation equivariant segmentation network. Our best model also outperforms the state-of-the-art challenging methods, which further demonstrate the effectiveness of our proposed deeply supervised rotation equivariant segmentation network.
Lesion Border Detection in Dermoscopy Images Using Ensembles of Thresholding Methods
Louisiana State Univ.（路易斯安那州立大学）；Univ. of Electronic Science and Technology of China（电子科技大学）；Univ. of Illinois（伊利诺伊大学）；Hosei Univ.（法政大学）；Loughborough Univ.（拉夫堡大学）
A Multi-Level Deep Ensemble Model for Skin Lesion Classification in Dermoscopy Images
National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology（空天地海一体化大数据应用技术国家工程实验室）；School of Computer Science and Engineering, Northwestern Polytechnical University（西北工业大学计算机学院）
The identification of melanoma involves an integrated analysis of skin lesion images acquired using the clinical and dermoscopy modalities. Dermoscopic images provide a detailed view of the subsurface visual structures that supplement the macroscopic clinical images. Melanoma diagnosis is commonly based on the 7-point visual category checklist (7PC). The 7PC contains intrinsic relationships between categories that can aid classification, such as shared features, correlations, and the contributions of categories towards diagnosis. Manual classification is subjective and prone to intra- and inte…
Institute of Medical Technology, Hamburg University of Technology（汉堡工业大学医学技术研究所）
The initial assessment of skin lesions is typically based on dermoscopic images. As this is a difficult and time-consuming task, machine learning methods using dermoscopic images have been proposed to assist human experts. Other approaches have studied electrical impedance spectroscopy (EIS) as a basis for clinical decision support systems. Both methods represent different ways of measuring skin lesion properties as dermoscopy relies on visible light and EIS uses electric currents. Thus, the two methods might carry complementary features for lesion classification. Therefore, we propose joint d…
Exploiting learning algorithms under scarce data regimes is a limitation and a reality of the medical imaging field. In an attempt to mitigate the problem, we propose a data augmentation protocol based on generative adversarial networks. We condition the networks at a pixel-level (segmentation mask) and at a global-level information (acquisition environment or lesion type). Such conditioning provides immediate access to the image-label pairs while controlling global class specific appearance of the synthesized images. To stimulate synthesis of the features relevant for the segmentation task, a…
Automatic Detection of Blue-White Veil and Related Structures in Dermoscopy Images
Louisiana State University in Shreveport（路易斯安那州立大学什里夫波特分校）；Hosei University（法政大学）；Stoecker & Associates（斯托克联合公司）；Missouri University of Science and Technology（密苏里科技大学）；University of Miami School of Medicine（迈阿密大学医学院）；Second University of Naples（那不勒斯第二大学）；Queensland Institute of Dermatology, University of Queensland（昆士兰大学昆士兰皮肤病研究所）
Unsupervised and semi-supervised learning with Categorical Generative Adversarial Networks assisted by Wasserstein distance for dermoscopy image Classification
University of Saskatchewan（萨斯喀彻温大学）；Philips Canada（加拿大飞利浦）
Beihang University（北京航空航天大学）；Shanghai Aerospace Control Technology Institute（上海航天控制技术研究所）；Peking Union Medical College Hospital（北京协和医院）；Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院）
The computer-aided diagnosis (CAD) system can provide a reference basis for the clinical diagnosis of skin diseases. Convolutional neural networks (CNNs) can not only extract visual elements such as colors and shapes but also semantic features. As such they have made great improvements in many tasks of dermoscopy images. The imaging of dermoscopy has no principal orientation, indicating that there are a large number of skin lesion rotations in the datasets. However, CNNs lack rotation invariance, which is bound to affect the robustness of CNNs against rotations. To tackle this issue, we propos…
Samsung Advanced Institute of Health Sciences and Technology（三星健康科学与技术高级研究院）；Sungkyunkwan University（成均馆大学）；Samsung Medical Center（三星医疗中心）
As skin cancer is one of the most frequent cancers globally, accurate, non-invasive dermoscopy-based diagnosis becomes essential and promising. A task of the Part 3 of the ISIC Skin Image Analysis Challenge at MICCAI 2018 is to predict seven disease classes with skin lesion images, including melanoma (MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic keratosis / Bowen's disease (intraepithelial carcinoma) (AKIEC), benign keratosis (solar lentigo / seborrheic keratosis / lichen planus-like keratosis) (BKL), dermatofibroma (DF) and vascular lesion (VASC) as defined by the Interna…
Deep Learning Ensembles for Melanoma Recognition in Dermoscopy Images
IBM
黑色素瘤是最致命的皮肤癌。虽然可以通过早期发现治愈，但只有训练有素的专家才能准确识别这种疾病。由于专业知识供应有限，能够识别疾病的自动化系统可以挽救生命，减少不必要的活检，并降低成本。为了实现这一目标，我们提出了一个系统，该系统将深度学习的最新发展与已建立的机器学习方法相结合，创建能够分割皮肤病变的方法集合，以及分析检测到的区域和周围组织以进行黑色素瘤检测。该系统使用最大的公开可用的皮肤镜图像基准数据集进行评估，包含900个训练图像和379个测试图像。新的国家的最先进的性能水平得到证明，导致在7.5%的接收器工作特性曲线下的面积的改善（0.843 vs. 0.783），平均精度为4%（0.649对0.624），在临床相关的95%灵敏度操作点测量的特异性是先前最新技术水平的2.9倍（36.8%特异性对比12.5%）。与平均8个专家皮肤科医生对100个测试图像的子集相比，所提出的系统产生了更高的准确性（76%与70.5%）和特异性（62%与59%），在同等的灵敏度（82%）。
Shiraz branch, Islamic Azad University（伊斯兰阿扎德大学设拉子分校）；Legal Medicine Research Center, Iranian Legal Medicine Organization（伊朗法医组织法医学研究中心）
In this paper, the effectiveness and capability of convolutional neural networks have been studied in the classification of 8 skin diseases. Different pre-trained state-of-the-art architectures (DenseNet 201, ResNet 152, Inception v3, InceptionResNet v2) were used and applied on 10135 dermoscopy skin images in total (HAM10000: 10015, PH2: 120). The utilized dataset includes 8 diagnostic categories - melanoma, melanocytic nevi, basal cell carcinoma, benign keratosis, actinic keratosis and intraepithelial carcinoma, dermatofibroma, vascular lesions, and atypical nevi. The aim is to compare the a…
Automated delineation of COVID-19 lesions from lung CT scans aids the diagnosis and prognosis for patients. The asymmetric shapes and positioning of the infected regions make the task extremely difficult. Capturing information at multiple scales will assist in deciphering features, at global and local levels, to encompass lesions of variable size and texture. We introduce the Full-scale Deeply Supervised Attention Network (FuDSA-Net), for efficient segmentation of corona-infected lung areas in CT images. The model considers activation responses from all levels of the encoding path, encompassin…
Department of CS & IT, SUIT Peshawar（白沙瓦SUIT计算机科学与信息技术系）
Skin lesion segmentation can be rather a challenging task owing to the presence of artifacts, low contrast between lesion and boundary, color variegation, fuzzy skin lesion borders and heterogeneous background in dermoscopy images. In this paper, we propose a simple yet effective saliency-based approach derived in the frequency and spatial domain to detect pigmented skin lesion. Two color models are utilized for the construction of these maps. We suggest a different metric for each color model to design map in the spatial domain via color features. The map in the frequency domain is generated …
Institute of Computing (IC)（计算学院）；School of Electrical and Computing Engineering (FEEC)（电气与计算工程学院）；RECOD Lab.（RECOD实验室）；University of Campinas (UNICAMP)（坎皮纳斯大学）
Hospital Clínic Barcelona, Universitat de Barcelona, IDIBAPS（巴塞罗那临床医院，巴塞罗那大学，IDIBAPS）；IBM Research AI, T J Watson Research Center（IBM研究AI，T J Watson研究中心）；Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center（医学部皮肤科，纪念斯隆-凯特琳癌症中心）；Kitware；Signal Theory and Communications, Universitat Politècnica de Catalunya（信号理论与通信，加泰罗尼亚理工大学）；Melanoma Unit, Dermatology Department, Hospital Clínic Barcelona, Universitat de Barcelona, IDIBAPS（黑素瘤科，皮肤科，巴塞罗那临床医院，巴塞罗那大学，IDIBAPS）
This article summarizes the BCN20000 dataset, composed of 19424 dermoscopic images of skin lesions captured from 2010 to 2016 in the facilities of the Hospital Clínic in Barcelona. With this dataset, we aim to study the problem of unconstrained classification of dermoscopic images of skin cancer, including lesions found in hard-to-diagnose locations (nails and mucosa), large lesions which do not fit in the aperture of the dermoscopy device, and hypo-pigmented lesions. The BCN20000 will be provided to the participants of the ISIC Challenge 2019, where they will be asked to train algorithms to c…
Yale University（耶鲁大学）；Cleerly Inc（Cleerly公司）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；East China University of Science and Technology（华东理工大学）
Melanoma is a type of skin cancer with the most rapidly increasing incidence. Early detection of melanoma using dermoscopy images significantly increases patients' survival rate. However, accurately classifying skin lesions by eye, especially in the early stage of melanoma, is extremely challenging for the dermatologists. Hence, the discovery of reliable biomarkers will be meaningful for melanoma diagnosis. Recent years, the value of deep learning empowered computer-assisted diagnose has been shown in biomedical imaging based decision making. However, much research focuses on improving disease…
Duke University（杜克大学）；Duke Institute for Health Innovation（杜克健康创新研究所）
We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images, which can be indistinctly acquired via smartphone or dermoscopy capture. Additionally, we do not assume that images contain single lesions, thus the framework supports both focal or wide-field images. Specifically, we propose a two-stage approach in which we first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of…
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Science and Technology of China（中国科学技术大学）
Skin lesion segmentation is an important step for automatic melanoma diagnosis. Due to the non-negligible diversity of lesions from different patients, extracting powerful context for fine-grained semantic segmentation is still challenging today. Although the deep convolutional neural network (CNNs) have made significant improvements on skin lesion segmentation, they often fail to reserve the spatial details and long-range dependencies context due to consecutive convolution striding and pooling operations inside CNNs. In this paper, we formulate a cascaded context enhancement neural network fo…
GraphDerm: Fusing Imaging, Physical Scale, and Metadata in a Population-Graph Classifier for Dermoscopic Lesions
School of Computer Science, Institute for Research in Fundamental Sciences (IPM)（计算机科学学院，基础科学研究所（IPM））；Department of Physics, Shahid Beheshti University（物理系，肖德·贝赫谢什提大学）；Faculty of Science, University of Tehran（科学学院，德黑兰大学）；Fatemeh Zahra Hospital, Alborz University of Medical Sciences（法特梅·扎拉医院，阿尔bor兹医学科学大学）；Department of Electrical and Computer Engineering, Sungkyunkwan University（电气与计算机工程系，成均馆大学）；Department of Computer Science and Engineering, Shahid Beheshti University（计算机科学与工程系，肖德·贝赫谢什提大学）；Autoimmune Bullous Diseases Research Center, Razi Hospital, Tehran University of Medical Sciences（自身免疫性水疱病研究中心，拉齐医院，德黑兰医学科学大学）
FH Joanneum University of Applied Sciences（约阿内姆应用科学大学）
Skin cancer is the most common cancer worldwide, with melanoma being the deadliest form. Dermoscopy is a skin imaging modality that has shown an improvement in the diagnosis of skin cancer compared to visual examination without support. We evaluate the current state of the art in the classification of dermoscopic images based on the ISIC-2019 Challenge for the classification of skin lesions and current literature. Various deep neural network architectures pre-trained on the ImageNet data set are adapted to a combined training data set comprised of publicly available dermoscopic and clinical im…
Tarbiat Modares University（塔比阿特莫达雷斯大学）；NRP company（NRP公司）；University of Warwick（华威大学）；University of Sheffield（谢菲尔德大学）
Computer-aided diagnosis systems for classification of different type of skin lesions have been an active field of research in recent decades. It has been shown that introducing lesions and their attributes masks into lesion classification pipeline can greatly improve the performance. In this paper, we propose a framework by incorporating transfer learning for segmenting lesions and their attributes based on the convolutional neural networks. The proposed framework is based on the encoder-decoder architecture which utilizes a variety of pre-trained networks in the encoding path and generates t…
Xiamen University（厦门大学）；Xiamen University Malaysia（厦门大学马来西亚分校）；Manteia Technologies Co.,Ltd（曼泰亚科技有限公司）；The Chinese University of Hong Kong（香港中文大学）；The Hong Kong Polytechnic University（香港理工大学）
Skin lesion segmentation from dermoscopy images is of great importance for improving the quantitative analysis of skin cancer. However, the automatic segmentation of melanoma is a very challenging task owing to the large variation of melanoma and ambiguous boundaries of lesion areas. While convolutional neutral networks (CNNs) have achieved remarkable progress in this task, most of existing solutions are still incapable of effectively capturing global dependencies to counteract the inductive bias caused by limited receptive fields. Recently, transformers have been proposed as a promising tool …
Deeply Supervised Skin Lesions Diagnosis with Stage and Branch Attention
City University of Hong Kong（香港城市大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Shenzhen Research Institute, City University of Hong Kong（香港城市大学深圳研究院）
Nanyang Technological University（南洋理工大学）；University of British Columbia（不列颠哥伦比亚大学）；Vancouver Coast Health Research Institute（温哥华海岸健康研究所）；BC Cancer（不列颠哥伦比亚癌症中心）
Skin cancer is one of the most common types of malignancy, affecting a large population and causing a heavy economic burden worldwide. Over the last few years, computer-aided diagnosis has been rapidly developed and make great progress in healthcare and medical practices due to the advances in artificial intelligence. However, most studies in skin cancer detection keep pursuing high prediction accuracies without considering the limitation of computing resources on portable devices. In this case, knowledge distillation (KD) has been proven as an efficient tool to help improve the adaptability o…
Adapting Segment Anything Model 3 for Concept-Driven Lesion Segmentation in Medical Images: An Experimental Study
The Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心放射肿瘤学系医学人工智能与自动化（MAIA）实验室）；Medical Image Processing Group, Department of Radiology, University of Pennsylvania（宾夕法尼亚大学放射学系医学图像处理组）；Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心内科学系消化与肝脏疾病科）
Central Electronics Engineering Research Institute, CSIR Madras Complex（CSIR马德拉斯综合体中央电子工程研究所）；University of Bridgeport（桥港大学）
Image segmentation and classification are the two main fundamental steps in pattern recognition. To perform medical image segmentation or classification with deep learning models, it requires training on large image dataset with annotation. The dermoscopy images (ISIC archive) considered for this work does not have ground truth information for lesion segmentation. Performing manual labelling on this dataset is time-consuming. To overcome this issue, self-learning annotation scheme was proposed in the two-stage deep learning algorithm. The two-stage deep learning algorithm consists of U-Net seg…
University of Barcelona（巴塞罗那大学）；Universitat Rovira i Virgili（罗维拉-维尔吉利大学）；Khalifa University of Science and Technology（哈利法科学技术大学）；Mil-kin Inc.（Mil-kin公司）；Dhaka Medical College Hospital（达卡医学院附属医院）；University of Warwick（华威大学）；University of Toulouse（图卢兹大学）
The determination of precise skin lesion boundaries in dermoscopic images using automated methods faces many challenges, most importantly, the presence of hair, inconspicuous lesion edges and low contrast in dermoscopic images, and variability in the color, texture and shapes of skin lesions. Existing deep learning-based skin lesion segmentation algorithms are expensive in terms of computational time and memory. Consequently, running such segmentation algorithms requires a powerful GPU and high bandwidth memory, which are not available in dermoscopy devices. Thus, this article aims to achieve …
IAFI-FCOS: Intra- and across-layer feature interaction FCOS model for lesion detection of CT images
College of Computer Science and Technology, Zhejiang University of Technology（浙江工业大学计算机科学与技术学院）；The First Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院附属第一医院）
Xiamen University（厦门大学）；Manteia Technologies Co.,Ltd.（Manteia科技有限公司）；Fujian Cancer Hospital（福建省肿瘤医院）；Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health)（瓯江实验室（浙江省再生医学、视觉与脑健康实验室））；Wenzhou Institute, University of Chinese Academy of Sciences（中国科学院大学温州研究院）；West China Hospital, Sichuan University（四川大学华西医院）；The Hong Kong Polytechnic University（香港理工大学）
Skin lesion segmentation from dermoscopy images is of great significance in the quantitative analysis of skin cancers, which is yet challenging even for dermatologists due to the inherent issues, i.e., considerable size, shape and color variation, and ambiguous boundaries. Recent vision transformers have shown promising performance in handling the variation through global context modeling. Still, they have not thoroughly solved the problem of ambiguous boundaries as they ignore the complementary usage of the boundary knowledge and global contexts. In this paper, we propose a novel cross-scale …
Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram（印度信息技术与设计制造学院，坎奇普拉姆）
Skin cancer is the most common cancer in the existing world constituting one-third of the cancer cases. Benign skin cancers are not fatal, can be cured with proper medication. But it is not the same as the malignant skin cancers. In the case of malignant melanoma, in its peak stage, the maximum life expectancy is less than or equal to 5 years. But, it can be cured if detected in early stages. Though there are numerous clinical procedures, the accuracy of diagnosis falls between 49% to 81% and is time-consuming. So, dermoscopy has been brought into the picture. It helped in increasing the accur…
