Fluorescence lifetime imaging microscopy and its applications in skin cancer diagnosis

Fluorescence lifetime(FLT)of fluorophores is sensitive to the changes in their surrounding microenvironment,and hence it can quantitatively reveal the physiological characterization of the tissue under investigation.Fluorescence lifetime imaging microscopy(FLIM)provides not only morphological but also functional information of the tisse by producing spatially resolved image of fuorophore lifetime,which can be used as a signature of disorder and/or malignancy in diseased tissues.In this paper,we begin by introducing the basic principle and common detection methods of FLIM.Then the recent advances in the FLIM-based diagnosis of three different skin cancers,including basal cell carcinoma(BCC),squamous cell carcinoma(SCC)and malignant melanoma(MM)are reviewed.Furthermore,the potential advantages of FLIM in skin cancer diagnosis and the challenges that may be faced in the future are prospected.

Faster Region Based Convolutional Neural Network for Skin Lesion Segmentation

The diagnostic interpretation of dermoscopic images is a complex task as it is very difficult to identify the skin lesions from the normal.Thus the accurate detection of potential abnormalities is required for patient monitoring and effec-tive treatment.In this work,a Two-Tier Segmentation(TTS)system is designed,which combines the unsupervised and supervised techniques for skin lesion seg-mentation.It comprises preprocessing by the medianfilter,TTS by Colour K-Means Clustering(CKMC)for initial segmentation and Faster Region based Con-volutional Neural Network(FR-CNN)for refined segmentation.The CKMC approach is evaluated using the different number of clusters(k=3,5,7,and 9).An inception network with batch normalization is employed to segment mel-anoma regions effectively.Different loss functions such as Mean Absolute Error(MAE),Cross Entropy Loss(CEL),and Dice Loss(DL)are utilized for perfor-mance evaluation of the TTS system.The anchor box technique is employed to detect the melanoma region effectively.The TTS system is evaluated using 200 dermoscopic images from the PH2 database.The segmentation accuracies are analyzed in terms of Pixel Accuracy(PA)and Jaccard Index(JI).Results show that the TTS system has 90.19%PA with 0.8048 JI for skin lesion segmentation using DL in FR-CNN with seven clusters in CKMC than CEL and MAE.

基于自反馈阈值学习的半监督皮肤癌诊断模型

为解决监督学习皮肤癌诊断模型的训练需要大量数据标注,且医学专家标注工作成本高、耗时长、易疲劳等问题,提出了一种基于自反馈阈值学习(Self-Feedback Threshold Learning,SFTL)的半监督皮肤癌诊断方法.在标注数据预训练的ResNet网络基础上,引入全局和局部类别间伪标签自反馈阈值学习机制动态筛选ResNet预测概率大于自反馈阈值的无标记样本,引入无监督阈值学习损失和分类交叉熵损失进行模型训练,在标记样本稀缺的情况下深入挖掘无标记数据的鉴别诊断信息,显著降低模型在无标记皮肤病变图像中的误判率.选取公开数据集HAM10000的皮肤病变图像展开实验验证,在仅需50%标记数据下实现了0.8229的准确率和0.7651的F1分数,证明所提出的SFTL模型在半监督场景下可有效解决皮肤癌诊断任务,相比其他同类方法具有更好的分类性能.

Fuzzy C Mean Thresholding based Level Set for Automated Segmentation of Skin Lesions

Accurate segmentation is an important and challenging task in any computer vision system. It also plays a vital role in computerized analysis of skin lesion images. This paper presents a new segmentation method that combines the advan-tages of fuzzy C mean algorithm, thresholding and level set method. 3-class Fuzzy C mean thresholding is applied to initialize level set automatically and also for estimating controlling parameters for level set evolution. Parameters for performance evaluation are presented and segmentation results are compared with some other state-of-the-art segmentation methods. Increased true detection rate and reduced false positive and false negative errors confirm the effectiveness of proposed method for skin cancer detection.

Arithmetic Optimization with Ensemble Deep Transfer Learning Based Melanoma Classification

Melanoma is a skin disease with high mortality rate while earlydiagnoses of the disease can increase the survival chances of patients. Itis challenging to automatically diagnose melanoma from dermoscopic skinsamples. Computer-Aided Diagnostic (CAD) tool saves time and effort indiagnosing melanoma compared to existing medical approaches. In this background,there is a need exists to design an automated classification modelfor melanoma that can utilize deep and rich feature datasets of an imagefor disease classification. The current study develops an Intelligent ArithmeticOptimization with Ensemble Deep Transfer Learning Based MelanomaClassification (IAOEDTT-MC) model. The proposed IAOEDTT-MC modelfocuses on identification and classification of melanoma from dermoscopicimages. To accomplish this, IAOEDTT-MC model applies image preprocessingat the initial stage in which Gabor Filtering (GF) technique is utilized.In addition, U-Net segmentation approach is employed to segment the lesionregions in dermoscopic images. Besides, an ensemble of DL models includingResNet50 and ElasticNet models is applied in this study. Moreover, AOalgorithm with Gated Recurrent Unit (GRU) method is utilized for identificationand classification of melanoma. The proposed IAOEDTT-MC methodwas experimentally validated with the help of benchmark datasets and theproposed model attained maximum accuracy of 92.09% on ISIC 2017 dataset.

烧伤瘢痕与皮肤慢性溃疡恶变的早期诊断与治疗

目的 :通过对 12例烧伤瘢痕与皮肤慢性溃疡恶变的早期诊断与治疗临床经验总结 ,提高对经久不愈的溃汤及易于破溃而不稳定的挛缩瘢痕发生恶性变认识的可能性 ,尽早诊断和治疗。方法 :对经久不愈的溃疡和瘢痕 ,在不激惹局部组织的情况下 ,进行组织活检 ,一旦确诊恶变 ,即刻手术治疗。结果 :本组 12例术后均一期愈合。经 2年～ 8年随访 ,9例患者未见手术局部复发和病灶转移 ,3例因心脑血管病死亡。结论 :凡临床上遇到久治不愈的慢性皮肤病损和易于破溃而不稳定的挛缩瘢痕 ,均有恶变的可能性 ,临床应密切观察 。

皮肤黑色素瘤流行病学及防治研究进展

皮肤黑色素瘤是一种致命性的皮肤恶性肿瘤。在过去的二十多年里,皮肤黑色素瘤的患病率处于明显的上升趋势。皮肤黑色素瘤已成为严重的公共卫生问题,引起了社会的广泛关注。本文就近年来皮肤黑色素瘤的流行病学和防治进行综述。本综述的目的在于通过介绍皮肤黑色素瘤相关内容,帮助临床医生更好地了解该疾病的现状。

融合迁移学习和数据增强的SC-Net模型在皮肤癌识别中的应用

为了解决皮肤癌诊断模型中性能无法满足临床应用要求,对于少数类别诊断精度不高的问题,提出一种基于迁移学习和数据增强的皮肤癌诊断模型SC-Net(skin cancer-net)。首先,引入ECA注意力模块,把DenseNet-201在ImageNet数据集上的预训练模型在皮肤癌数据集上进行微调训练并提取图像隐含高层次特征;然后融合一般性统计特征,并且通过SMOTE过采样技术以增强少数类别数据;最后,将数据输入XGBoost模型进行训练,最终得到SC-Net分类模型。实验结果表明,SC-Net模型在准确率、灵敏度、特异度三个指标上达到99.25%、99.25%和99.88%,诊断准确率相对于已有文献精度提升约0.6%~18.7%,并且对于皮肤纤维瘤、光化性角化病等少数类别具备更强的分类能力。

乳腺癌皮肤转移患者误诊为皮脂腺囊肿伴感染一例诊断过程分析

乳腺癌是女性发病率和死亡率最高的恶性肿瘤,乳腺癌皮肤转移在临床上并不常见,且容易造成误诊。本文报道了1例于2019-02-25入住中国人民解放军第518医院的乳腺癌皮肤转移患者的诊断经过以及被误诊为皮脂腺囊肿伴感染的文献分析。初步探讨了乳腺癌皮肤转移复杂多样的临床表现以及发病机制,以提高临床医生对乳腺癌皮肤转移的认识,当诊断困难时需结合电子计算机断层扫描(CT)、病理学检查辅助诊断,从而早发现、早治疗,以免造成误诊、漏诊。

基于计算机辅助诊断的皮肤癌良恶性诊断研究

皮肤癌实验中的皮肤性疾病,皮肤癌的早期筛查对于临床干预治疗至关重要.为提高皮肤癌的精准预测,减少发病率和死亡率,本文在残差网络迁移学习模型的基础上,构建一种皮肤癌良恶性判断的计算机辅助诊断网络.实验结果表明,基于残差网络的预测模型,计算机辅助诊断模型的训练集预测精度为84%,测试集预测精度达到90%.基于计算机辅助诊断的皮肤癌良恶性预测诊断模型为临床皮肤癌早期筛查提供借鉴.

Acral melanoma detection using dermoscopic images and convolutional neural networks

Acral melanoma(AM)is a rare and lethal type of skin cancer.It can be diagnosed by expert dermatologists,using dermoscopic imaging.It is challenging for dermatologists to diagnose melanoma because of the very minor differences between melanoma and non-melanoma cancers.Most of the research on skin cancer diagnosis is related to the binary classification of lesions into melanoma and non-melanoma.However,to date,limited research has been conducted on the classification of melanoma subtypes.The current study investigated the effectiveness of dermoscopy and deep learning in classifying melanoma subtypes,such as,AM.In this study,we present a novel deep learning model,developed to classify skin cancer.We utilized a dermoscopic image dataset from the Yonsei University Health System South Korea for the classification of skin lesions.Various image processing and data augmentation techniques have been applied to develop a robust automated system for AM detection.Our custombuilt model is a seven-layered deep convolutional network that was trained from scratch.Additionally,transfer learning was utilized to compare the performance of our model,where AlexNet and ResNet-18 were modified,fine-tuned,and trained on the same dataset.We achieved improved results from our proposed model with an accuracy of more than 90%for AM and benign nevus,respectively.Additionally,using the transfer learning approach,we achieved an average accuracy of nearly 97%,which is comparable to that of state-of-the-art methods.From our analysis and results,we found that our model performed well and was able to effectively classify skin cancer.Our results show that the proposed system can be used by dermatologists in the clinical decision-making process for the early diagnosis of AM.

高频超声在皮肤恶性肿瘤中的应用

高频超声技术是传统超声技术的延伸,因其具有高分辨率,可以更好评估浅表结构,与传统超声相比,在皮肤疾病的诊断中具有更广阔的应用前景。高频超声操作简单、具有无创性,能够清晰地将皮肤病变部位显现出来,并且对病变的形状、大小及浸润度进行测量,进一步提升皮肤恶性肿瘤检测的准确率,同时对手术切除提供病灶的详实数据。本文就高频超声在皮肤恶性肿瘤诊断、治疗中的应用进行综述。

血清CLDN2、DPT、SFRP2联合检测法诊断早期结直肠癌的效果分析

目的:探讨用血清封闭蛋白2(CLDN2)、皮肤蛋白(DPT)、分泌型卷曲相关蛋白2(SFRP2)联合检测法诊断早期结直肠癌的效果。方法:选取2016年10月至2018年10月期间常州市金坛区人民医院收治的30例早期结直肠癌患者、30例结直肠良性疾病患者及同期在该院进行体检的30例健康人作为本次研究的对象。将其中的30例早期结直肠癌患者设为恶性肿瘤组,将其中的30例结直肠良性疾病患者设为良性疾病组,将其中的30例健康人设为对照组。三组受检者入院后,均检测其血清CLDN2、DPT、SFRP2的水平。用ROC曲线下面积分析用CLDN2、DPT、SFRP2联合检测法诊断早期结直肠癌的效果。结果:恶性肿瘤组患者血清CLDN2的水平均高于良性疾病组患者和对照组健康人,P<0.05。良性疾病组患者血清CLDN2的水平高于对照组健康人,P<0.05。恶性肿瘤组患者血清DPT、SFRP2的水平均低于良性疾病组患者和对照组健康人,P<0.05。良性疾病组患者血清DPT、SFRP2的水平均低于对照组健康人,P<0.05。用血清CLDN2、DPT、SFRP2联合检测法诊断早期结直肠癌的灵敏度为76.67%,特异度为90%,准确率为85.56%。结论:用血清CLDN2、DPT、SFRP2联合检测法诊断早期结直肠癌的效果较为理想。

11例皮肤瘢痕癌的诊疗体会

目的总结11例皮肤瘢痕癌的诊疗经验。方法11例患者均行局部病灶组织活检确诊，然后予行扩大切除术联合皮瓣和（或）厚皮片移植修复，合并区域淋巴结肿大者加行区域淋巴结清扫术；因局部癌肿固定而怀疑癌细胞残存者，术后加行局部放射治疗。所有患者均行术后随访观察。结果本组病例至少表现4种临床类型，即溃疡型、菜花型、膨隆型和混合型。经术后0．5～17年随访观察，全部患者中有2例出现病变复发，其中1例经再度扩大切除术加皮瓣转移术治疗，术后随访0．5年无复发，另l例死于其他全身性合并疾病；其余10例均健在，功能和外观恢复满意。结论手术扩大切除加皮瓣和（或）皮片移植是瘢痕癌的首选有效治疗方法。本病重在预防，不稳定型瘢痕的治疗并防止瘢痕溃疡的发生，是预防瘢痕癌变的重要措施。

推扫式高光谱显微成像系统设计与实验

高光谱显微成像(HMI)是一种新型无损光学诊断技术,其光谱数据能够反映样本的内部微环境变化,图像数据可以反映样本空间结构信息,因此可以作为癌症诊断工具,在未来具有广阔的应用前景。但HMI数据量大且数据结构复杂,将其应用于癌症诊断领域需要进行系统详细的数据解译。设计并搭建了一套推扫式HMI系统,并编写了系统控制、数据采集和数据分析软件,可提供多种基于机器学习的数据处理方法。基于MATLAB编制了具有图形化用户界面的HMI数据采集和数据分析软件,该软件可给出分析结果,为医生病理诊断提供了便利。利用该系统和软件进行皮肤癌的分类与分期研究,验证了系统的性能。HMI系统的光谱范围为465.5~905.1nm,光谱分辨率约为3nm,视场尺寸为400.18μm×192.47μm,放大倍率为28.15,实际分辨率范围为1.10~1.38μm。分别采集基底细胞癌、鳞状细胞癌和恶性黑色素瘤组织的HMI数据,利用图像数据实现了三种皮肤癌的分类,准确率为85%;利用光谱数据实现了鳞状细胞癌的分期鉴别,准确率达到96.4%。