生成学习是表征学习的一个强大工具，在生物医学成像问题上表现出了特别的希望。然而，在这种情况下，从分布中采样是次要的，以找到真实图像的表示，这往往带有标签，并明确表示目标分布的内容和质量。从生成模型忠实地重建图像仍然很困难，特别是那些像组织学图像一样复杂的模型。在这项工作中，结合了两种现有的方法（自动编码器和对抗性潜伏自动编码器），试图提高我们对非黑色素瘤皮肤癌，特别是表皮内癌（IEC）的真实图像进行编码和解码的能力。利用IEC（256 x 256）的高质量图像数据集，这项工作评估了图像重建质量和表示学习的结果。研究表明，对抗性训练可以将基线FID分数从76提高到50，并且表征学习的基准可以提高3%。平滑和现实的插值的形态结构的变化也首次提出，定位表示学习作为一个有前途的方向，在计算病理学的背景下。
英文摘要：
Generative learning is a powerful tool for representation learning, and shows particular promise for problems in biomedical imaging. However, in this context, sampling from the distribution is secondary to finding representations of real images, which often come with labels and explicitly represent the content and quality of the target distribution. It remains difficult to faithfully reconstruct images from generative models, particularly those as complex as histological images. In this work, two existing methods (autoencoders and adversarial latent autoencoders) are combined in attempt to improve our ability to encode and decode real images of non-melanoma skin cancer, specifically intra-epidermal carcinoma (IEC). Utilising a dataset of high-quality images of IEC (256 x 256), this work assesses the result of both image reconstruction quality and representation learning. It is shown that adversarial training can improve baseline FID scores from 76 to 50, and that benchmarks on representation learning can be improved by up to 3%. Smooth and realistic interpolations of the variation in the morphological structure are also presented for the first time, positioning representation learning as a promising direction in the context of computational pathology.
The Fast and Accurate Approach to Detection and Segmentation of Melanoma Skin Cancer using Fine-tuned Yolov3 and SegNet Based on Deep Transfer Learning
黑色素瘤是最严重的皮肤癌之一，可以发生在人类皮肤的任何部分。黑色素瘤病变的早期诊断将大大增加其治愈的机会。改善黑色素瘤分割将有助于医生或手术机器人更准确地从身体部位切除病变。近年来，基于学习的图像分割方法在图像分割中取得了较好的效果。这项研究提出了一种新的方法，通过定义一个基于深度学习模型的两步管道来改善黑色素瘤皮肤病变的检测和分割。我们的方法在ISIC 2018（皮肤病变分析对黑色素瘤检测挑战数据集）众所周知的数据集上进行了评估。所提出的方法包括两个主要部分的实时检测病变的位置和分割。在检测部分，皮肤病变的位置由微调的You Only Look Once第3版（F-YOLOv 3）精确检测，然后输入微调的分割网络（F-SegNet）。皮肤病变定位有助于减少用于分割的整个图像的不必要的计算。结果表明，我们提出的F-YOLOv 3在平均精度（mAP）方面表现更好，达到96%。与最先进的分割方法相比，我们的F-SegNet实现了更高的性能，准确率为95.16%。
SkinGenBench: Generative Model and Preprocessing Effects for Synthetic Dermoscopic Augmentation in Melanoma Diagnosis
Alliance School of Advanced Computing, Alliance University, India（印度联盟大学高级计算学院）；Department of Computer Science and Communication, Østfold University College, 1783 Halden, Norway（挪威东福尔大学学院计算机科学与传播系）；Department of Dentistry and Oral Health, Aarhus University, 8000 Aarhus, Denmark（丹麦奥胡斯大学牙科与口腔健康系）
Early Melanoma Diagnosis with Sequential Dermoscopic Images
Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University（郑州大学公共卫生学院流行病学与生物统计学系）；Victorian Melanoma Service, Alfred Health（阿尔弗雷德健康服务中心维多利亚黑色素瘤中心）；eResearch Centre, Monash University（莫纳什大学电子研究中心）
Deep Clustering via Center-Oriented Margin Free-Triplet Loss for Skin Lesion Detection in Highly Imbalanced Datasets
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）
U-Net-based Models for Skin Lesion Segmentation: More Attention and Augmentation
K N Toosi University of Technology（K N Toosi工业大学）；University of Tehran（德黑兰大学）；Amirkabir University of Technology（Amirkabir工业大学）；Uniklinik RWTH Aachen（亚琛工业大学医院）
Application of Machine Learning in Melanoma Detection and the Identification of 'Ugly Duckling' and Suspicious Naevi: A Review
School of Electrical Engineering and Computer Science, University of Queensland（昆士兰大学电气工程与计算机科学学院）；Frazer Institute, The University of Queensland（昆士兰大学弗雷泽研究所）；Centre for Health Services Research, Faculty of Medicine, The University of Queensland（昆士兰大学医学院健康服务研究中心）
Leveraging Knowledge Distillation for Lightweight Skin Cancer Classification: Balancing Accuracy and Computational Efficiency
United International University（联合国际大学）；The University of Western Australia（西澳大学）；Bangladesh University of Business and Technology（孟加拉国商业与技术大学）；Ahsanullah University of Science and Technology（阿萨努拉科技大学）；Charles Darwin University（查尔斯达尔文大学）
A Pathology Deep Learning System Capable of Triage of Melanoma Specimens Utilizing Dermatopathologist Consensus as Ground Truth
Proscia Inc.（Proscia公司）；University of Florida College of Medicine（佛罗里达大学医学院）；Sidney Kimmel Medical College at Thomas Jefferson University（托马斯杰斐逊大学西德尼·金梅尔医学院）；Cedars-Sinai（西达赛奈医疗中心）
Using Computer Vision for Skin Disease Diagnosis in Bangladesh Enhancing Interpretability and Transparency in Deep Learning Models for Skin Cancer Classification
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）
A Patient-Centric Dataset of Images and Metadata for Identifying Melanomas Using Clinical Context
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）
Artificial Intelligence-Based Image Classification for Diagnosis of Skin Cancer: Challenges and Opportunities
Dartmouth College（达特茅斯学院）；Dartmouth-Hitchcock Medical Center（达特茅斯-希区柯克医疗中心）；Geisel School of Medicine（盖塞尔医学院）；Metrohealth System（都会医疗系统）；Case Western Reserve University（凯斯西储大学）
A Wavelet Guided Attention Module for Skin Cancer Classification with Gradient-based Feature Fusion
Jadavpur University（jadavpur大学）；International Institute of Information Technology（国际信息技术学院）；China University of Mining and Technology（中国矿业大学）；Saint Petersburg Electrotechnical University "LETI"（圣彼得堡电工技术大学“LETI”）
