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نتیجه جستجو - Melanoma

تعداد مقالات یافته شده: 9
ردیف عنوان نوع
1 Transform domain representation-driven convolutional neural networks for skin lesion segmentation
انتقال شبکه های عصبی کانولوشن نمایندگی محور دامنه برای تقسیم بندی ضایعه پوستی-2020
Automated diagnosis systems provide a huge improvement in early detection of skin cancer, and con- sequently, contribute to successful treatment. Recent research on convolutional neural network has achieved enormous success in segmentation and object detection tasks. However, these networks require large amount of data that is a big challenge in medical domain where often have insufficient data and even a pretrained model on medical images can be hardly found. Lesion segmentation as the initial step of skin cancer analysis remains a challenging issue since datasets are small and include a variety of im- ages in terms of light, color, scale, and marks which have led researchers to use extensive augmentation and preprocessing techniques or fine tuning the network with a pretrained model on irrelevant images. A segmentation model based on convolutional neural networks is proposed in this study for the tasks of skin lesion segmentation and dermoscopic feature segmentation. The network is trained from scratch and despite the small size of datasets neither excessive data augmentation nor any preprocessing to remove artifacts or enhance the images are applied. Alternatively, we investigated incorporating image represen- tations of the transform domain to the convolutional neural network and compared to a model with more convolutional layers that resulted in 6% higher Jaccard index and has shorter training time. The model improved by applying CIELAB color space and the performance of the final proposed architecture is evaluated on publicly available datasets from ISBI challenges in 2016 and 2017. The proposed model has resulted in an improvement of as much as 7% for the segmentation metrics and 17% for the fea- ture segmentation, which demonstrates the robustness of this unique hybrid framework and its future applications as well as further improvement.
Keywords: Convolutional neural network | Dermoscopic features | Melanoma | Skin lesion segmentation | Transform domain
مقاله انگلیسی
2 Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images
Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images-2019
Abstract Background: The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports on 25e26% of discordance for classifying a benign nevus versus malignant melanoma. A recent study indicated the potential of deep learning to lower these discordances. However, the performance of deep learning in classifying histopathologic melanoma images was never compared directly to human experts. The aim of this study is to perform such a first direct comparison. Methods: A total of 695 lesions were classified by an expert histopathologist in accordance with current guidelines (350 nevi/345 melanoma). Only the haematoxylin & eosin (H&E) slides of these lesions were digitalised via a slide scanner and then randomly cropped. A total of 595 of the resulting images were used to train a convolutional neural network (CNN). The additional 100 H&E image sections were used to test the results of the CNN in comparison to 11 histopathologists. Three combined McNemar tests comparing the results of the CNNs test runs in terms of sensitivity, specificity and accuracy were predefined to test for significance (p < 0.05). Findings: The CNN achieved a mean sensitivity/specificity/accuracy of 76%/60%/68% over 11 test runs. In comparison, the 11 pathologists achieved a mean sensitivity/specificity/accuracy of 51.8%/66.5%/59.2%. Thus, the CNN was significantly (p Z 0.016) superior in classifying the cropped images. Interpretation: With limited image information available, a CNN was able to outperform 11 histopathologists in the classification of histopathological melanoma images and thus shows promise to assist human melanoma diagnoses.
KEYWORDS : Melanoma | Pathology | Histopathology | Deep learning | Artificial intelligence
مقاله انگلیسی
3 Skin cancer detection by deep learning and sound analysis algorithms: A prospective clinical study of an elementary dermoscope
تشخیص سرطان پوست با الگوریتم های یادگیری عمیق و آنالیز صدا: یک مطالعه بالینی آینده نگر از یک درموسکوپ ابتدایی-2019
Background: Skin cancer (SC), especiallymelanoma, is a growing public health burden. Experimental studies have indicated a potential diagnostic role for deep learning (DL) algorithms in identifying SC at varying sensitivities. Previously, it was demonstrated that diagnostics by dermoscopy are improved by applying an additional sonification (data to sound waves conversion) layer on DL algorithms. The aim of the study was to determine the impact of image quality on accuracy of diagnosis by sonification employing a rudimentary skin magnifier with polarized light (SMP). Methods: Dermoscopy images acquired by SMP were processed by a first deep learning algorithm and sonified. Audio output was further analyzed by a different secondary DL. Study criteria outcomes of SMP were specificity and sensitivity,which were further processed by a F2-score, i.e. applying a twice extra weight to sensitivity over positive predictive values. Findings: Patients (n=73) fulfilling inclusion criteriawere referred to biopsy. SMP analysis metrics resulted in a receiver operator characteristic curve AUCs of 0.814 (95% CI, 0.798–0.831). SMP achieved a F2-score sensitivity of 91.7%, specificity of 41.8% and positive predictive value of 57.3%.Diagnosing the sameset of patients lesions by an advanced dermoscope resulted in a F2-score sensitivity of 89.5%, specificity of 57.8% and a positive predictive value of 59.9% (P=NS). Interpretation: DL processing of dermoscopic images followed by sonification results in an accurate diagnostic output for SMP, implying that the quality of the dermoscope is not the major factor influencing DL diagnosis of skin cancer. Present system might assist all healthcare providers as a feasible computer-assisted detection system. Fund: Bostel Technologies.
Keywords: Skin cancer | Deep learning | Dermoscopy | Sonification | Melanoma | Telemedicine | Artificial intelligence
مقاله انگلیسی
4 Sequence and expression analysis of the cytoplasmic pattern recognition receptor melanoma differentiation-associated gene 5 from the barbel chub Squaliobarbus curriculus
Sequence and expression analysis of the cytoplasmic pattern recognition receptor melanoma differentiation-associated gene 5 from the barbel chub Squaliobarbus curriculus-2019
MDA5 is a cytoplasmic viral double-stranded RNA recognition receptor that plays a pivotal role in the aquatic animal innate immune system. To decipher the role of MDA5 of Squaliobarbus curriculus (ScMDA5) in the immune response, full-length cDNA of ScMDA5 was cloned using the RACE technology, mRNA and protein expression levels of ScMDA5 signalling pathway members in response to stimulation were detected and effects of overexpression of ScMDA5 on the immune response were investigated. ScMDA5 comprises 3597 bp and is composed of an open reading frame (2958 nucleotides long) that translates into a putative peptide of 985 amino acid residues. ScMDA5 possesses two N-terminal caspase-recruiting domains, DEAD-like helicases superfamily, helicase superfamily C-terminal and RIG-I_C-RD domains, and differences in these domains among species were mainly observed with respect to their length and location. ScMDA5 was closely clustered with those of Carassius auratus, Ctenopharyngodon idellus and Mylopharyngodon piceus. ScMDA5 transcripts were most abundant in the spleen and the lowest in the liver. Expression levels of ScMDA5 in healthy tissues were significantly correlated with those of ScIRF3, ScIRF7 and ScIFN. Besides, mRNA expression levels of ScIRF3 were significantly correlated with those of ScIRF7 (0.956, P < 0.01). Expression level changes, including downregulation, upregulation and initial upregulation followed by downregulation, were found in ScMDA5 signalling pathway molecules in tissues after grass carp reovirus infection. Protein levels of ScMDA5 were the highest in the liver and the lowest in the spleen in detected healthy tissues. Overexpression of ScMDA5 led to significantly enhanced CiIRF7 and CiMx transcription in grass carp ovary cells (P < 0.05). The results of this study helped to clarify the role of ScMDA5 in the immune reaction against grass carp reovirus and provided fundamental information for fish breeding to achieve strong resistance to infection.
Keywords: MDA5 | Squaliobarbus curriculus | Grass carp reovirus | Expression level | Overexpression
مقاله انگلیسی
5 A comparative study of deep learning architectures on melanoma detection
مطالعه تطبیقی معماریهای یادگیری عمیق در تشخیص ملانوما-2019
Melanoma is the most aggressive type of skin cancer, which significantly reduces the life expectancy. Early detection of melanoma can reduce the morbidity and mortality associated with skin cancer. Dermoscopic images acquired by dermoscopic instruments are used in computational analysis for skin cancer detection. However, some image quality limitations such as noises, shadows, artefacts exist that could compromise the robustness of the skin image analysis. Hence, developing an automatic intelligent system for skin cancer diagnosis with accurate detection rate is crucial. In this paper, we evaluate the performance of several state-of-the-art convolutional neural networks in dermoscopic images of skin lesions. Our experiment is conducted on a graphics processing unit (GPU) to speed up the training and deployment process. To enhance the quality of images, we employ different pre-processing steps. We also apply data augmentation methodology such as horizontal and vertical flipping techniques to address the class skewness problem. Both pre-processing and data augmentation could help to improve the final accuracy.
Keywords: Cancer classification | Computational diagnosis | Convolutional neural networks | Deep learning | Melanoma detection
مقاله انگلیسی
6 Temporal phenotyping by mining healthcare data to derive lines of therapy for cancer
فنوتیپ موقتی با استخراج داده های مراقبت های بهداشتی برای استخراج خطوط درمانی برای سرطان-2019
Lines of therapy (LOT) derived from real-world healthcare data not only depict real-world cancer treatment sequences, but also help define patient phenotypes along the course of disease progression and therapeutic interventions. The sequence of prescribed anticancer therapies can be defined as temporal phenotyping resulting from changes in morphological (tumor staging), biochemical (biomarker testing), physiological (disease progression), and behavioral (physician prescribing and patient adherence) parameters. We introduce a novel methodology that is a two-part approach: 1) create an algorithm to derive patient-level LOT and 2) aggregate LOT information via clustering to derive temporal phenotypes, in conjunction with visualization techniques, within a large insurance claims dataset. We demonstrated the methodology using two examples: metastatic nonsmall cell lung cancer and metastatic melanoma. First, we generated a longitudinal patient cohort for each cancer type and applied a set of rules to derive patient-level LOT. Then the LOT algorithm outputs for each cancer type were visualized using Sankey plots and K-means clusters based on durations of LOT and of gaps in therapy between LOT. We found differential distribution of temporal phenotypes across clusters. Our approach to identify temporal patient phenotypes can increase the quality and utility of analyses conducted using claims datasets, with the potential for application to multiple oncology disease areas across diverse healthcare data sources. The understanding of LOT as defining patients’ temporal phenotypes can contribute to continuous health learning of disease progression and its interaction with different treatment pathways; in addition, this understanding can provide new insights that can be applied by tailoring treatment sequences for the patient phenotypes who will benefit.
Keywords: Claims database | K-means clustering analysis | Oncology line of therapy | Patient-level | Temporal phenotyping | Treatment sequence
مقاله انگلیسی
7 Analysis of Raman spectroscopy data with algorithms based on paraconsistent logic for characterization of skin cancer lesions
تجزیه و تحلیل داده های طیف سنجی رامان با الگوریتم های مبتنی بر منطق paraconsistent برای توصیف ضایعات سرطان پوست-2019
Analysis of the Raman data to obtain results in discrimination models is usually done with multivariate statistics based on principal component analysis (PCA). In this work, we present a technique based on a non-classical logic called paraconsistent logic (PL). The aim of this work is to use computational procedures capable of generating efficient expert systems to discriminate cutaneous tissue samples obtained by Raman spectroscopy. First, a set of algorithms originating from PL is presented, and then its application in discrimination analyses is described; the discrimination analysis was conducted using a database of skin tissue samples obtained ex vivo by Raman spectroscopy of spectrum range of 400–1800 cm−1 wavelengths. Data processing, pattern creation, and comparisons were performed using the set of paraconsistent algorithms (SPA-PAL2v). The total number of samples was divided into four histopathological groups, with 115 spectra of basal cell carcinoma (BCC), 21 spectra of squamous cell carcinoma (SCC), 57 spectra of actinic keratosis (AK), and 30 normal skin (NO) spectra. An arrangement type was created for this study, and the samples were randomly selected and analyzed, and the NO group was compared with the group of non-melanoma cancer lesions (BCC+SCC) and the AK tumor lesion. Two analyses were performed. The first (SPA-PAL2v) Mode 1 (no cross-validation) achieved 76% of hits, and the second (SPA-PAL2v) Mode 2 (with cross-validation) achieved 75.78% of hits. These results were compared with discrimination using PCA statistical methods (PCA/DA) and presented superior percentages of hits, which proves the robustness of the SPA-PAL2v, confirming its potential for Raman spectrum data analysis.
Keywords: Raman spectroscopy | Algorithms | Skin cancer | Paraconsistent annotated logic | Medical diagnosis
مقاله انگلیسی
8 Neuronal transcriptomic responses to Japanese encephalitis virus infection with a special focus on chemokine CXCL11 and pattern recognition receptors RIG-1 and MDA5
پاسخهای رونویسی عصبی به عفونت ویروس آنسفالیت ژاپنی با تمرکز ویژه روی کموکاین CXCL11 و گیرنده های تشخیص الگوی RIG-1 و MDA5-2019
Japanese encephalitis virus (JEV) causes central nervous system neuronal injury and inflammation. A clear understanding of neuronal responses to JEV infection remains elusive. Using the Affymetrix array to investigate the transcriptome of infected SK-N-MC cells, 1316 and 2737 dysregulated genes (≥ 2/−2 fold change, P < 0.05) were found at 48 hours post-infection (hpi) and 60 hpi, respectively. The genes were mainly involved in anti-microbial responses, cell signalling, cellular function and maintenance, and cell death and survival. Among the most highly upregulated genes (≥ 10 folds, P < 0.05) were chemokines CCL5, CXCL11, IL8 and CXCL10. The upregulation and expression of CXCL11 were confirmed by qRT-PCR and immunofluorescence. Pathogen recognition receptors retinoic acid-inducible gene-1 (RIG-1) and melanoma differentiation-associated protein 5 (MDA5) were also upregulated. Our results strongly suggest that neuronal cells play a significant role in immunity against JEV. CXCL11, RIG-1 and MDA5 and other cytokines may be important in neuropathogenesis.
Keywords: Japanese encephalitis virus | Transcriptome | RNA microarray | Proinflammatory mediators | Neuronal infection | CXCL11 | RIG-1 | MDA5
مقاله انگلیسی
9 Multi-Class Multi-Level Classification Algorithm for Skin Lesions Classification using Machine Learning Techniques
الگوریتم طبقه بندی چند مرحله ای چند سطح برای طبقه بندی ضایعات پوستی با استفاده از تکنیک های یادگیری ماشین-2019
Skin diseases remain a major cause of disability worldwide and contribute approximately 1.79% of the global burden of disease measured in disability-adjusted life years. In the United Kingdom alone, 60% of the population suffer from skin diseases during their lifetime. In this paper, we propose an intelligent digital diagnosis scheme to improve the classification accuracy of multiple diseases. A Multi-Class Multi-Level (MCML) classification algorithm inspired by the “divide and conquer” rule is explored to address the research challenges. The MCML classification algorithm is implemented using traditional machine learning and advanced deep learning approaches. Improved techniques are proposed for noise removal in the traditional machine learning approach. The proposed algorithm is evaluated on 3672 classified images, collected from different sources and the diagnostic accuracy of 96.47% is achieved. To verify the performance of the proposed algorithm, its metrics are compared with the Multi-Class Single-Level classification algorithm which is the main algorithm used in most of the existing literature. The results also indicate that the MCML classification algorithm is capable of enhancing the classification performance of multiple skin lesions.
Keywords: skin lesion classification | computer-aided diagnosis | machine learning | deep learning | texture & colour features | melanoma classification | eczema classification
مقاله انگلیسی
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