Background: Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, automated analysis of dermoscopy images has become an important research area. Border detection is often the first step in this analysis. Methods: In this article, we present an approximate lesion localization method that serves as a preprocessing step for detecting borders in dermoscopy images. In this method, first the black frame around the image is removed using an iterative algorithm. The approximate location of the lesion is then determined using an ensemble of thresholding algorithms. Results: The method is tested on a set of 428 dermoscopy images. The localization error is quantified by a metric that uses dermatologist determined borders as the ground truth. Conclusion: The results demonstrate that the method presented here achieves both fast and accurate localization of lesions in dermoscopy images.
This report summarises our method and validation results for the ISIC Challenge 2018 - Skin Lesion Analysis Towards Melanoma Detection - Task 1: Lesion Segmentation. We present a two-stage method for lesion segmentation with optimised training method and ensemble post-process. Our method achieves state-of-the-art performance on lesion segmentation and we win the first place in ISIC 2018 task1.
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）
Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, automated analysis of dermoscopy images has become an important research area. Border detection is often the first step in this analysis. In many cases, the lesion can be roughly separated from the background skin using a thresholding method applied to the blue channel. However, no single thresholding method appears to be robust enough to successfully handle the wide variety of dermoscopy images encountered in clin…
Background: Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, computerized analysis of dermoscopy images has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion borders. Methods: In this article, we present a systematic overview of the recent border detection methods in the literature paying particular attention to computational issues and evaluation aspects. Conclusion: Common proble…
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）
A multi-level deep ensemble (MLDE) model that can be trained in an 'end to end' manner is proposed for skin lesion classification in dermoscopy images. In this model, four pre-trained ResNet-50 networks are used to characterize the multiscale information of skin lesions and are combined by using an adaptive weighting scheme that can be learned during the error back propagation. The proposed MLDE model achieved an average AUC value of 86.5% on the ISIC-skin 2018 official validation dataset, which is substantially higher than the average AUC values achieved by each of four ResNet-50 networks.
Malignant melanoma has one of the most rapidly increasing incidences in the world and has a considerable mortality rate. Early diagnosis is particularly important since melanoma can be cured with prompt excision. Dermoscopy images play an important role in the non-invasive early detection of melanoma [1]. However, melanoma detection using human vision alone can be subjective, inaccurate and poorly reproducible even among experienced dermatologists. This is attributed to the challenges in interpreting images with diverse characteristics including lesions of varying sizes and shapes, lesions tha…
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 …
Background: Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, dermoscopy image analysis has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion borders. Although numerous methods have been developed for the detection of lesion borders, very few studies were comprehensive in the evaluation of their results. Methods: In this paper, we evaluate five recent border detection methods on a s…
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…
Skin cancer is one of the most common forms of cancer and its incidence is projected to rise over the next decade. Artificial intelligence is a viable solution to the issue of providing quality care to patients in areas lacking access to trained dermatologists. Considerable progress has been made in the use of automated applications for accurate classification of skin lesions from digital images. In this manuscript, we discuss the design and implementation of a deep learning algorithm for classification of dermoscopy images from the HAM10000 Dataset. We trained a convolutional neural network b…
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…
Indian Statistical Institute（印度统计研究所）；Indian Institute of Technology, Guwahati（印度理工学院古瓦哈提分校）
In this paper, a deep neural network based ensemble method is experimented for automatic identification of skin disease from dermoscopic images. The developed algorithm is applied on the task3 of the ISIC 2018 challenge dataset (Skin Lesion Analysis Towards Melanoma Detection).
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…
We propose a novel approach to identify one of the most significant dermoscopic criteria in the diagnosis of Cutaneous Melanoma: the Blue-whitish structure. In this paper, we achieve this goal in a Multiple Instance Learning framework using only image-level labels of whether the feature is present or not. As the output, we predict the image classification label and as well localize the feature in the image. Experiments are conducted on a challenging dataset with results outperforming state-of-the-art. This study provides an improvement on the scope of modelling for computerized image analysis …
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）
Dermoscopy is a non-invasive skin imaging technique, which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. One of the most important features for the diagnosis of melanoma in dermoscopy images is the blue-white veil (irregular, structureless areas of confluent blue pigmentation with an overlying white "ground-glass" film). In this article, we present a machine learning approach to the detection of blue-white veil and related structures in dermoscopy images. The method involves contextual pixel classification using…
University of Saskatchewan（萨斯喀彻温大学）；Philips Canada（加拿大飞利浦）
Melanoma is a curable aggressive skin cancer if detected early. Typically, the diagnosis involves initial screening with subsequent biopsy and histopathological examination if necessary. Computer aided diagnosis offers an objective score that is independent of clinical experience and the potential to lower the workload of a dermatologist. In the recent past, success of deep learning algorithms in the field of general computer vision has motivated successful application of supervised deep learning methods in computer aided melanoma recognition. However, large quantities of labeled images are re…
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…
This paper proposes an innovative method for segmentation of skin lesions in dermoscopy images developed by the authors, based on fuzzy classification of pixels and histogram thresholding.
Stoecker & Associates；Louisiana State University（路易斯安那州立大学）
The incidence of malignant melanoma continues to increase worldwide. This cancer can strike at any age; it is one of the leading causes of loss of life in young persons. Since this cancer is visible on the skin, it is potentially detectable at a very early stage when it is curable. New developments have converged to make fully automatic early melanoma detection a real possibility. First, the advent of dermoscopy has enabled a dramatic boost in clinical diagnostic ability to the point that melanoma can be detected in the clinic at the very earliest stages. The global adoption of this technology…
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…
Melanoma is the deadliest form of skin cancer. While curable with early detection, only highly trained specialists are capable of accurately recognizing the disease. As expertise is in limited supply, automated systems capable of identifying disease could save lives, reduce unnecessary biopsies, and reduce costs. Toward this goal, we propose a system that combines recent developments in deep learning with established machine learning approaches, creating ensembles of methods that are capable of segmenting skin lesions, as well as analyzing the detected area and surrounding tissue for melanoma …
University of Electronic Science and Technology of China（电子科技大学）
Dermoscopy image detection stays a tough task due to the weak distinguishable property of the object.Although the deep convolution neural network signifigantly boosted the performance on prevelance computer vision tasks in recent years,there remains a room to explore more robust and precise models to the problem of low contrast image segmentation.Towards the challenge of Lesion Segmentation in ISBI 2017,we built a symmetrical identity inception fully convolution network which is based on only 10 reversible inception blocks,every block composed of four convolution branches with combination of d…
University of Electronic Science and Technology of China（电子科技大学）
With a large influx of dermoscopy images and a growing shortage of dermatologists, automatic dermoscopic image analysis plays an essential role in skin cancer diagnosis. In this paper, a new deep fully convolutional neural network (FCNN) is proposed to automatically segment melanoma out of skin images by end-to-end learning with only pixels and labels as inputs. Our proposed FCNN is capable of using both local and global information to segment melanoma by adopting skipping layers. The public benchmark database consisting of 150 validation images, 600 test images and 2000 training images in the…
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 …
Localizing thoracic diseases on chest X-ray plays a critical role in clinical practices such as diagnosis and treatment planning. However, current deep learning based approaches often require strong supervision, e.g. annotated bounding boxes, for training such systems, which is infeasible to harvest in large-scale. We present Probabilistic Class Activation Map (PCAM) pooling, a novel global pooling operation for lesion localization with only image-level supervision. PCAM pooling explicitly leverages the excellent localization ability of CAM during training in a probabilistic fashion. Experimen…
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…
Robert Bosch GmbH（罗伯特·博世有限公司）；TU Dortmund University（多特蒙德工业大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Convolutional neural networks (CNNs) show impressive performance for image classification and detection, extending heavily to the medical image domain. Nevertheless, medical experts are sceptical in these predictions as the nonlinear multilayer structure resulting in a classification outcome is not directly graspable. Recently, approaches have been shown which help the user to understand the discriminative regions within an image which are decisive for the CNN to conclude to a certain class. Although these approaches could help to build trust in the CNNs predictions, they are only slightly sho…
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）
VUNO Inc.（VUNO公司）；Seoul National University College of Medicine（首尔大学医学院）；Seoul National University Bundang Hospital（首尔大学盆唐医院）
Fundoscopic images are often investigated by ophthalmologists to spot abnormal lesions to make diagnoses. Recent successes of convolutional neural networks are confined to diagnoses of few diseases without proper localization of lesion. In this paper, we propose an efficient annotation method for localizing lesions and a CNN architecture that can classify an individual finding and localize the lesions at the same time. Also, we introduce a new loss function to guide the network to learn meaningful patterns with the guidance of the regional annotations. In experiments, we demonstrate that our n…
Multimodal self-supervised learning for lesion localization
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院保罗·C·劳特伯生物医学成像研究中心）；Peng Cheng Laboratory（鹏城实验室）；University of Chinese Academy of Sciences（中国科学院大学）；The University of Hong Kong（香港大学）；Pazhou Laboratory (Huangpu)（琶洲实验室（黄埔））；Nanchang University（南昌大学）
Skin Lesion Correspondence Localization in Total Body Photography
Johns Hopkins University（约翰斯·霍普金斯大学）；Lumo Imaging；Johns Hopkins School of Medicine（约翰斯·霍普金斯医学院）；Eunice Kennedy Shriver National Institute of Child Health and Human Development（尤尼斯·肯尼迪·施莱弗国家儿童健康与人类发展研究所）；Johns Hopkins University, Department of Orthopaedic Surgery（约翰斯·霍普金斯大学骨科）
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…
TWLR: Text-Guided Weakly-Supervised Lesion Localization and Severity Regression for Explainable Diabetic Retinopathy Grading
Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science（广东省级交叉学科研究与数据科学应用重点实验室）；Department of Statistics and Data Science, Beijing Normal-Hong Kong Baptist University（北京师范大学-香港 Baptist大学统计与数据科学系）；Faculty of Science, Hong Kong Baptist University（香港 Baptist大学科学学院）；Data Science Research Center, Duke Kunshan University（杜克-昆山大学数据科学研究中心）；Shanxi Provincial People’s Hospital（山西人民医院）；The Fifth Clinical Medical school of Shanxi Medical University（山西医科大学第五临床医学院）；Research Center for Mathematics, Beijing Normal University（北京师范大学数学研究中心）；Department of Mathematics and Statistics, York University（约克大学数学与统计学系）
Interpretability of deep learning (DL) systems is gaining attention in medical imaging to increase experts' trust in the obtained predictions and facilitate their integration in clinical settings. We propose a deep visualization method to generate interpretability of DL classification tasks in medical imaging by means of visual evidence augmentation. The proposed method iteratively unveils abnormalities based on the prediction of a classifier trained only with image-level labels. For each image, initial visual evidence of the prediction is extracted with a given visual attribution technique. T…
An approach to lesion recognition is described that for lesion localization uses an ensemble of segmentation techniques and for lesion classification an exhaustive structural analysis. For localization, candidate regions are obtained from global thresholding of the chromatic maps and from applying the K-Means algorithm to the RGB image; the candidate regions are then integrated. For classification, a relatively exhaustive structural analysis of contours and regions is carried out.
Renmin University of China（中国人民大学）；Beijing Visionary Intelligence Ltd.（北京远景智能有限公司）；Peking Union Medical College Hospital（北京协和医院）
Over 300 million people worldwide are affected by various retinal diseases. By noninvasive Optical Coherence Tomography (OCT) scans, a number of abnormal structural changes in the retina, namely retinal lesions, can be identified. Automated lesion localization in OCT is thus important for detecting retinal diseases at their early stage. To conquer the lack of manual annotation for deep supervised learning, this paper presents a first study on utilizing semi-supervised object detection (SSOD) for lesion localization in OCT images. To that end, we develop a taxonomy to provide a unified and stru…
Institute for Medical Engineering and Science（医学工程与科学研究所）；Massachusetts Institute of Technology（麻省理工学院）；Lyft Inc.（来福特公司）；Neuromation OU（神经自动化有限公司）；University of Michigan（密歇根大学）
Accurate detection and localization for angiodysplasia lesions is an important problem in early stage diagnostics of gastrointestinal bleeding and anemia. Gold-standard for angiodysplasia detection and localization is performed using wireless capsule endoscopy. This pill-like device is able to produce thousand of high enough resolution images during one passage through gastrointestinal tract. In this paper we present our winning solution for MICCAI 2017 Endoscopic Vision SubChallenge: Angiodysplasia Detection and Localization its further improvements over the state-of-the-art results using sev…
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 …
We propose a novel convolutional neural network for lesion detection from weak labels. Only a single, global label per image - the lesion count - is needed for training. We train a regression network with a fully convolutional architecture combined with a global pooling layer to aggregate the 3D output into a scalar indicating the lesion count. When testing on unseen images, we first run the network to estimate the number of lesions. Then we remove the global pooling layer to compute localization maps of the size of the input image. We evaluate the proposed network on the detection of enlarged…
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）
The University of Edinburgh（爱丁堡大学）；Canon Medical Research Europe（佳能医疗欧洲研究院）；University of Glasgow（格拉斯哥大学）；The Alan Turing Institute（艾伦·图灵研究所）
Reducing the requirement for densely annotated masks in medical image segmentation is important due to cost constraints. In this paper, we consider the problem of inferring pixel-level predictions of brain lesions by only using image-level labels for training. By leveraging recent advances in generative diffusion probabilistic models (DPM), we synthesize counterfactuals of "How would a patient appear if X pathology was not present?". The difference image between the observed patient state and the healthy counterfactual can be used for inferring the location of pathology. We generate counterfac…
Stroke Locus Net: Occluded Vessel Localization from MRI Modalities
Department of Electrical Engineering, College of Engineering, Qatar University, Doha, Qatar（电气工程系，工程学院，卡塔尔大学，多哈，卡塔尔）；Department of Mathematics and Statistics, College of Arts and Sciences, Qatar University, Doha, Qatar（数学与统计学系，艺术与科学学院，卡塔尔大学，多哈，卡塔尔）
本研究提出端到端深度学习流水线 Stroke Locus Net，结合 nnUNet 分割、动脉图谱与 pGAN 生成，仅用 MRI 扫描实现闭塞血管定位，为更快速的中风诊断提供潜力。
