Minimizing false negative rate in melanoma detection and providing insight into the causes of classification
Eötvös University（厄特沃什大学）；Wigner Research Centre for Physics（维格纳物理研究中心）；Semmelweis University（塞麦尔维斯大学）；German Research Center for Artificial Intelligence（德国人工智能研究中心）
Ellák Somfai, Benjámin Baffy, Kristian Fenech, Changlu Guo, Rita Hosszú, Dorina Korózs, Fabrizio Nunnari, Marcell Pólik, Daniel Sonntag, Attila Ulbert, András Lőrincz
Our goal is to bridge human and machine intelligence in melanoma detection. We develop a classification system exploiting a combination of visual pre-processing, deep learning, and ensembling for providing explanations to experts and to minimize false negative rate while maintaining high accuracy in melanoma detection. Source images are first automatically segmented using a U-net CNN. The result of the segmentation is then used to extract image sub-areas and specific parameters relevant in human evaluation, namely center, border, and asymmetry measures. These data are then processed by tailored neural networks which include structure searching algorithms. Partial results are then ensembled by a committee machine. Our evaluation on the largest skin lesion dataset which is publicly available today, ISIC-2019, shows improvement in all evaluated metrics over a baseline using the original images only. We also showed that indicative scores computed by the feature classifiers can provide useful insight into the various features on which the decision can be based.
Memorial Sloan Kettering Cancer Center（纪念斯隆-凯特琳癌症中心）；The University of Queensland（昆士兰大学）；University of Athens Medical School（雅典大学医学院）；IBM Research（IBM研究院）；Universitat de Barcelona（巴塞罗那大学）；IDIBAPS（IDIBAPS（生物医学研究所））；Melanoma Institute Australia（澳大利亚黑色素瘤研究所）；Emory University School of Medicine（埃默里大学医学院）；Medical University of Vienna（维也纳医科大学）；Mayo Clinic（梅奥诊所）；SUNY Downstate Medical School（纽约州立大学唐斯泰特医学院）；Stony Brook Medical School（石溪大学医学院）；Rabin Medical Center（拉宾医疗中心）；Weill Cornell Medical College（威尔康奈尔医学院）
Prior skin image datasets have not addressed patient-level information obtained from multiple skin lesions from the same patient. Though artificial intelligence classification algorithms have achieved expert-level performance in controlled studies examining single images, in practice dermatologists base their judgment holistically from multiple lesions on the same patient. The 2020 SIIM-ISIC Melanoma Classification challenge dataset described herein was constructed to address this discrepancy between prior challenges and clinical practice, providing for each image in the dataset an identifier …
Skin lesion datasets consist predominantly of normal samples with only a small percentage of abnormal ones, giving rise to the class imbalance problem. Also, skin lesion images are largely similar in overall appearance owing to the low inter-class variability. In this paper, we propose a two-stage framework for automatic classification of skin lesion images using adversarial training and transfer learning toward melanoma detection. In the first stage, we leverage the inter-class variation of the data distribution for the task of conditional image synthesis by learning the inter-class mapping a…
Amasya University（阿马西亚大学）；Bilkent University（比尔肯特大学）；National Magnetic Resonance Research Center, Bilkent University（比尔肯特大学国家磁共振研究中心）；Sabuncu Brain Research Center, Bilkent University（比尔肯特大学萨本朱脑研究中心）
Melanoma is a fatal skin cancer that is curable and has dramatically increasing survival rate when diagnosed at early stages. Learning-based methods hold significant promise for the detection of melanoma from dermoscopic images. However, since melanoma is a rare disease, existing databases of skin lesions predominantly contain highly imbalanced numbers of benign versus malignant samples. In turn, this imbalance introduces substantial bias in classification models due to the statistical dominance of the majority class. To address this issue, we introduce a deep clustering approach based on the …
University of Sydney（悉尼大学）；Shanghai Jiao Tong University（上海交通大学）
Multi-modality Fluorodeoxyglucose (FDG) positron emission tomography / computed tomography (PET/CT) has been routinely used in the assessment of common cancers, such as lung cancer, lymphoma, and melanoma. This is mainly attributed to the fact that PET/CT combines the high sensitivity for tumor detection of PET and anatomical information from CT. In PET/CT image assessment, automatic tumor segmentation is an important step, and in recent years, deep learning based methods have become the state-of-the-art. Unfortunately, existing methods tend to over-segment the tumor regions and include region…
Tlemcen University（特莱姆森大学）；Université de Bourgogne（勃艮第大学）
During the last years, computer vision-based diagnosis systems have been widely used in several hospitals and dermatology clinics, aiming at the early detection of malignant melanoma tumor, which is among the most frequent types of skin cancer. In this work, we present an automated diagnosis system based on the ABCD rule used in clinical diagnosis in order to discriminate benign from malignant skin lesions. First, to reduce the influence of small structures, a preprocessing step based on morphological and fast marching schemes is used. In the second step, an unsupervised approach for lesion se…
Computer Vision Center (CVC)（计算机视觉中心（CVC））；Univ. Autònoma of Barcelona (UAB)（巴塞罗那自治大学（UAB））
Autonomous driving systems require huge amounts of data to train. Manual annotation of this data is time-consuming and prohibitively expensive since it involves human resources. Therefore, active learning emerged as an alternative to ease this effort and to make data annotation more manageable. In this paper, we introduce a novel active learning approach for object detection in videos by exploiting temporal coherence. Our active learning criterion is based on the estimated number of errors in terms of false positives and false negatives. The detections obtained by the object detector are used …
Gwangju Institute of Science and Technology（光州科学技术院）
Abnormal activity detection is one of the most challenging tasks in the field of computer vision. This study is motivated by the recent state-of-art work of abnormal activity detection, which utilizes both abnormal and normal videos in learning abnormalities with the help of multiple instance learning by providing the data with video-level information. In the absence of temporal-annotations, such a model is prone to give a false alarm while detecting the abnormalities. For this reason, in this paper, we focus on the task of minimizing the false alarm rate while performing an abnormal activity …
This paper presents an improved edge detection algorithm for facial and remotely sensed images using vector order statistics. The developed algorithm processes colored images directly without been converted to gray scale. A number of the existing algorithms converts the colored images into gray scale before detection of edges. But this process leads to inaccurate precision of recognized edges, thus producing false and broken edges in the output edge map. Facial and remotely sensed images consist of curved edge lines which have to be detected continuously to prevent broken edges. In order to de…
Classification of Gleason Grading in Prostate Cancer Histopathology Images Using Deep Learning Techniques: YOLO, Vision Transformers, and Vision Mamba
School of Computer Engineering, Iran University of Science and Technology（伊朗科学技术大学计算机工程学院）；School of Electrical Engineering, Iran University of Science and Technology（伊朗科学技术大学电气工程学院）；School of Electrical Engineering, Shahrood Branch, Islamic Azad University（伊斯兰阿扎德大学沙赫鲁德分校电气工程学院）；Medical Sciences and Technologies Department, Science and Research Branch, Islamic Azad University（伊斯兰阿扎德大学科学研究分支医学科学与技术部门）
California Institute of Technology（加州理工学院）；University of Southern California（南加州大学）；Mississippi State University（密西西比州立大学）
Contrastive learning has recently demonstrated superior performance to supervised learning, despite requiring no training labels. We explore how contrastive learning can be applied to hundreds of thousands of unlabeled Mars terrain images, collected from the Mars rovers Curiosity and Perseverance, and from the Mars Reconnaissance Orbiter. Such methods are appealing since the vast majority of Mars images are unlabeled as manual annotation is labor intensive and requires extensive domain knowledge. Contrastive learning, however, assumes that any given pair of distinct images contain distinct sem…
Digital Image Forgery Detection Using Transfer Learning
Marmara University, Department of Computer Engineering（马尔马拉大学计算机工程系）；Loyola University Chicago, Department of Computer Science（芝加哥洛伊拉大学计算机科学系）；Biruni University, Department of Computer Engineering（比鲁尼大学计算机工程系）
