scholarly journals A patient-centric dataset of images and metadata for identifying melanomas using clinical context

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Veronica Rotemberg ◽  
Nicholas Kurtansky ◽  
Brigid Betz-Stablein ◽  
Liam Caffery ◽  
Emmanouil Chousakos ◽  
...  
Keyword(s):  
Contextual Information ◽  
Skin Lesions ◽  
Classification Algorithms ◽  
Clinical Context ◽  
Patient Level ◽  
Level Information ◽  
Melanoma Classification ◽  
Level Performance ◽  
Atypical Nevi ◽  
Patient Centric

AbstractPrior 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 allowing lesions from the same patient to be mapped to one another. This patient-level contextual information is frequently used by clinicians to diagnose melanoma and is especially useful in ruling out false positives in patients with many atypical nevi. The dataset represents 2,056 patients (20.8% with at least one melanoma, 79.2% with zero melanomas) from three continents with an average of 16 lesions per patient, consisting of 33,126 dermoscopic images and 584 (1.8%) histopathologically confirmed melanomas compared with benign melanoma mimickers.

Multi-level 3D Densenets for False-positive Reduction in Lung Nodule Detection Based on Chest Computed Tomography

2020 ◽  
Vol 16 (8) ◽  
pp. 1004-1021
Author(s):  
Xiaoqi Lu ◽  
Yu Gu ◽  
Lidong Yang ◽  
Baohua Zhang ◽  
Ying Zhao ◽  
...  
Keyword(s):  
False Positive ◽  
Contextual Information ◽  
Lung Nodule ◽  
Positive Sample ◽  
Lung Nodules ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Level Information ◽  
Multi Level ◽  
Cade System

Objective: False-positive nodule reduction is a crucial part of a computer-aided detection (CADe) system, which assists radiologists in accurate lung nodule detection. In this research, a novel scheme using multi-level 3D DenseNet framework is proposed to implement false-positive nodule reduction task. Methods: Multi-level 3D DenseNet models were extended to differentiate lung nodules from falsepositive nodules. First, different models were fed with 3D cubes with different sizes for encoding multi-level contextual information to meet the challenges of the large variations of lung nodules. In addition, image rotation and flipping were utilized to upsample positive samples which consisted of a positive sample set. Furthermore, the 3D DenseNets were designed to keep low-level information of nodules, as densely connected structures in DenseNet can reuse features of lung nodules and then boost feature propagation. Finally, the optimal weighted linear combination of all model scores obtained the best classification result in this research. Results: The proposed method was evaluated with LUNA16 dataset which contained 888 thin-slice CT scans. The performance was validated via 10-fold cross-validation. Both the Free-response Receiver Operating Characteristic (FROC) curve and the Competition Performance Metric (CPM) score show that the proposed scheme can achieve a satisfactory detection performance in the falsepositive reduction track of the LUNA16 challenge. Conclusion: The result shows that the proposed scheme can be significant for false-positive nodule reduction task.

scholarly journals Publisher Correction: Author Correction: A patient-centric dataset of images and metadata for identifying melanomas using clinical context

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Veronica Rotemberg ◽  
Nicholas Kurtansky ◽  
Brigid Betz-Stablein ◽  
Liam Caffery ◽  
Emmanouil Chousakos ◽  
...  
Keyword(s):  
Clinical Context ◽  
Patient Centric

A Correction to this paper has been published: https://doi.org/10.1038/s41597-021-00879-x.

scholarly journals ASCU-Net: Attention Gate, Spatial and Channel Attention U-Net for Skin Lesion Segmentation

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 501
Author(s):  
Xiaozhong Tong ◽  
Junyu Wei ◽  
Bei Sun ◽  
Shaojing Su ◽  
Zhen Zuo ◽  
...  
Keyword(s):  
Skin Lesion ◽  
Contextual Information ◽  
Skin Lesions ◽  
Medical Image Segmentation ◽  
Learning Networks ◽  
Wide Range ◽  
Accurate Performance ◽  
Relevant Field ◽  
Skin Smooth ◽  
Smooth Transitions

Segmentation of skin lesions is a challenging task because of the wide range of skin lesion shapes, sizes, colors, and texture types. In the past few years, deep learning networks such as U-Net have been successfully applied to medical image segmentation and exhibited faster and more accurate performance. In this paper, we propose an extended version of U-Net for the segmentation of skin lesions using the concept of the triple attention mechanism. We first selected regions using attention coefficients computed by the attention gate and contextual information. Second, a dual attention decoding module consisting of spatial attention and channel attention was used to capture the spatial correlation between features and improve segmentation performance. The combination of the three attentional mechanisms helped the network to focus on a more relevant field of view of the target. The proposed model was evaluated using three datasets, ISIC-2016, ISIC-2017, and PH2. The experimental results demonstrated the effectiveness of our method with strong robustness to the presence of irregular borders, lesion and skin smooth transitions, noise, and artifacts.

scholarly journals COMPARABILITY OF BIOLOGICAL AGING MEASURES IN THE NATIONAL HEALTH AND NUTRITION EXAMINATION STUDY, 1999-2002

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S479-S479
Author(s):  
Waylon J Hastings ◽  
Daniel Belsky ◽  
Idan Shalev
Keyword(s):  
Risk Factors ◽  
Telomere Length ◽  
Cellular Level ◽  
Biological Age ◽  
Effect Sizes ◽  
Chronological Age ◽  
Biological Aging ◽  
Patient Level ◽  
Age Related ◽  
Level Information

Abstract Biological processes of aging are thought to be modifiable causes of many chronic diseases. Measures of biological aging could provide sensitive endpoints for studies of risk factors hypothesized to shorten healthy lifespan and/or interventions that extend it. However, uncertainty remains about how to measure biological aging and if proposed measures assess the same thing. We tested four proposed measures of biological aging with available data from NHANES 1999-2002: Klemera-Doubal method (KDM) Biological Age, homeostatic dysregulation, Levine Method (LM) Biological Age, and leukocyte telomere length. All measures of biological aging were correlated with chronological age. KDM Biological Age, homeostatic dysregulation, and LM Biological Age were all significantly associated with each other, but were each not associated with telomere length. NHANES participants with older biological ages performed worse on tests of physical, cognitive, perceptual, and subjective functions known to decline with advancing chronological age and thought to mediate age-related disability. Further, NHANES participants with higher levels of exposure to life-course risk factors were measured as having older biological ages. In both sets of analyses, effect-sizes tended to be larger for KDM Biological Age, homeostatic dysregulation, and LM Biological Age as compared to telomere length. Composite measures combining cellular- and patient-level information tended to have the largest effect-sizes. The cellular-level aging biomarker telomere length may measure different aspects of the aging process relative to the patient-level physiological measures. Studies aiming to test if risk factors accelerate aging or if interventions may slow aging should not treat proposed measures of biological aging as interchangeable.

scholarly journals Feature Selection of Non-Dermoscopic Skin Lesion Images for Nevus and Melanoma Classification

Computation ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 41 ◽  
Author(s):  
Felicia Anisoara Damian ◽  
Simona Moldovanu ◽  
Nilanjan Dey ◽  
Amira S. Ashour ◽  
Luminita Moraru
Keyword(s):  
Fourier Transform ◽  
Area Under The Curve ◽  
Roc Curves ◽  
Noise Removal ◽  
Skin Lesions ◽  
Asymmetry Index ◽  
Color Distribution ◽  
Atypical Feature ◽  
Melanoma Classification ◽  
Selection Of

(1) Background: In this research, we aimed to identify and validate a set of relevant features to distinguish between benign nevi and melanoma lesions. (2) Methods: Two datasets with 70 melanomas and 100 nevi were investigated. The first one contained raw images. The second dataset contained images preprocessed for noise removal and uneven illumination reduction. Further, the images belonging to both datasets were segmented, followed by extracting features considered in terms of form/shape and color such as asymmetry, eccentricity, circularity, asymmetry of color distribution, quadrant asymmetry, fast Fourier transform (FFT) normalization amplitude, and 6th and 7th Hu’s moments. The FFT normalization amplitude is an atypical feature that is computed as a Fourier transform descriptor and focuses on geometric signatures of skin lesions using the frequency domain information. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were employed to ascertain the relevance of the selected features and their capability to differentiate between nevi and melanoma. (3) Results: The ROC curves and AUC were employed for all experiments and selected features. A comparison in terms of the accuracy and AUC was performed, and an evaluation of the performance of the analyzed features was carried out. (4) Conclusions: The asymmetry index and eccentricity, together with F6 Hu’s invariant moment, were fairly competent in providing a good separation between malignant melanoma and benign lesions. Also, the FFT normalization amplitude feature should be exploited due to showing potential in classification.

Disproportionality methods for pharmacovigilance in longitudinal observational databases

2011 ◽  
Vol 22 (1) ◽  
pp. 39-56 ◽  
Author(s):  
Ivan Zorych ◽  
David Madigan ◽  
Patrick Ryan ◽  
Andrew Bate
Keyword(s):  
Data Mining ◽  
Drug Safety ◽  
Spontaneous Report ◽  
Health Claims ◽  
Data Sources ◽  
Patient Level ◽  
Number Of Patients ◽  
Level Information ◽  
Report System ◽  
Safety Signals

Data mining disproportionality methods (PRR, ROR, EBGM, IC, etc.) are commonly used to identify drug safety signals in spontaneous report system (SRS) databases. Newer data sources such as longitudinal observational databases (LOD) provide time-stamped patient-level information and overcome some of the SRS limitations such as an absence of the denominator, total number of patients who consume a drug, and limited temporal information. Application of the disproportionality methods to LODs has not been widely explored. The scale of the LOD data provides an interesting computational challenge. Larger health claims databases contain information on more than 50 million patients and each patient has records for up to 10 years. In this article we systematically explore the application of commonly used disproportionality methods to simulated and real LOD data.

scholarly journals Correlation between dermoscopic and histopathological diagnoses of atypical nevi in a dermatology outpatient clinic of the Medical School of São José do Rio Preto, SP, Brazil

2013 ◽  
Vol 88 (2) ◽  
pp. 199-203 ◽  
Author(s):  
João Roberto Antonio ◽  
Rosa Maria Cordeiro Soubhia ◽  
Solange Corrêa Garcia Pires D'Avila ◽  
Adriana Cristina Caldas ◽  
Lívia Arroyo Trídico ◽  
...  
Keyword(s):  
Outpatient Clinic ◽  
Skin Lesions ◽  
Histopathological Diagnosis ◽  
Melanocytic Nevi ◽  
Pigmented Lesions ◽  
Highly Sensitive ◽  
Advanced Stages ◽  
Malignant Lesions ◽  
Atypical Nevi

BACKGROUND: The incidence of cutaneous melanoma is increasing worldwide. Since it is an aggressive neoplasm, it is difficult to treat in advanced stages; early diagnosis is important to heal the patient. Melanocytic nevi are benign pigmented skin lesions while atypical nevi are associated with the risk of developing melanoma because they have a different histological pattern than common nevi. Thus, the clinical diagnosis of pigmented lesions is of great importance to differentiate benign, atypical and malignant lesions. Dermoscopy appeared as an auxiliary test in vivo, playing an important role in the diagnosis of pigmented lesions, because it allows the visualization of structures located below the stratum corneum. It shows a new morphological dimension of these lesions to the dermatologist and allows greater diagnostic accuracy. However, histopathology is considered the gold standard for the diagnosis. OBJECTIVES: To establish the sensitivity and specificity of dermoscopy in the diagnosis of pigmented lesions suspected of malignancy (atypical nevi), comparing both the dermatoscopic with the histopathological diagnosis, at the Dermatology Service of the outpatient clinic of Hospital de Base, São José do Rio Preto, SP. METHODS: Analysis of melanocytic nevi by dermoscopy and subsequent biopsy on suspicion of atypia or if the patient so desires, for subsequent histopathological diagnosis. RESULTS: Sensitivity: 93%. Specificity: 42%. CONCLUSIONS: Dermoscopy is a highly sensitive method for the diagnosis of atypical melanocytic nevi. Despite the low specificity with many false positive diagnoses, the method is effective for scanning lesions with suspected features of malignancy.

scholarly journals Predicting the clinical management of skin lesions using deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kumar Abhishek ◽  
Jeremy Kawahara ◽  
Ghassan Hamarneh
Keyword(s):  
Machine Learning ◽  
Clinical Management ◽  
Skin Lesions ◽  
Skin Condition ◽  
Management Decision ◽  
Learning Approaches ◽  
Management Decisions ◽  
Automated Machine Learning ◽  
Management Recommendations ◽  
Level Performance

AbstractAutomated machine learning approaches to skin lesion diagnosis from images are approaching dermatologist-level performance. However, current machine learning approaches that suggest management decisions rely on predicting the underlying skin condition to infer a management decision without considering the variability of management decisions that may exist within a single condition. We present the first work to explore image-based prediction of clinical management decisions directly without explicitly predicting the diagnosis. In particular, we use clinical and dermoscopic images of skin lesions along with patient metadata from the Interactive Atlas of Dermoscopy dataset (1011 cases; 20 disease labels; 3 management decisions) and demonstrate that predicting management labels directly is more accurate than predicting the diagnosis and then inferring the management decision ($$13.73 \pm 3.93\%$$ 13.73 ± 3.93 % and $$6.59 \pm 2.86\%$$ 6.59 ± 2.86 % improvement in overall accuracy and AUROC respectively), statistically significant at $$p < 0.001$$ p < 0.001 . Directly predicting management decisions also considerably reduces the over-excision rate as compared to management decisions inferred from diagnosis predictions (24.56% fewer cases wrongly predicted to be excised). Furthermore, we show that training a model to also simultaneously predict the seven-point criteria and the diagnosis of skin lesions yields an even higher accuracy (improvements of $$4.68 \pm 1.89\%$$ 4.68 ± 1.89 % and $$2.24 \pm 2.04\%$$ 2.24 ± 2.04 % in overall accuracy and AUROC respectively) of management predictions. Finally, we demonstrate our model’s generalizability by evaluating on the publicly available MClass-D dataset and show that our model agrees with the clinical management recommendations of 157 dermatologists as much as they agree amongst each other.

scholarly journals B-FGC-Net: A Building Extraction Network from High Resolution Remote Sensing Imagery

2022 ◽  
Vol 14 (2) ◽  
pp. 269
Author(s):  
Yong Wang ◽  
Xiangqiang Zeng ◽  
Xiaohan Liao ◽  
Dafang Zhuang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Information Integration ◽  
Large Scale ◽  
Spatial Information ◽  
Contextual Information ◽  
Building Extraction ◽  
Remote Sensing Images ◽  
Deep Convolutional Neural Networks ◽  
Level Information

Deep learning (DL) shows remarkable performance in extracting buildings from high resolution remote sensing images. However, how to improve the performance of DL based methods, especially the perception of spatial information, is worth further study. For this purpose, we proposed a building extraction network with feature highlighting, global awareness, and cross level information fusion (B-FGC-Net). The residual learning and spatial attention unit are introduced in the encoder of the B-FGC-Net, which simplifies the training of deep convolutional neural networks and highlights the spatial information representation of features. The global feature information awareness module is added to capture multiscale contextual information and integrate the global semantic information. The cross level feature recalibration module is used to bridge the semantic gap between low and high level features to complete the effective fusion of cross level information. The performance of the proposed method was tested on two public building datasets and compared with classical methods, such as UNet, LinkNet, and SegNet. Experimental results demonstrate that B-FGC-Net exhibits improved profitability of accurate extraction and information integration for both small and large scale buildings. The IoU scores of B-FGC-Net on WHU and INRIA Building datasets are 90.04% and 79.31%, respectively. B-FGC-Net is an effective and recommended method for extracting buildings from high resolution remote sensing images.

scholarly journals Occurrences of Lumpy Skin Disease (LSD) in Cattle in Dinajpur Sadar of Bangladesh

2020 ◽  
Vol 7 (3) ◽  
pp. 445-455
Author(s):  
Sumon Sarkar ◽  
Mirza Mienur Meher ◽  
Mst Misrat Masuma Parvez ◽  
Mahfuza Akther
Keyword(s):  
Skin Disease ◽  
Viral Disease ◽  
Skin Lesions ◽  
Young Female ◽  
Whole Body ◽  
Economic Losses ◽  
Local Breed ◽  
Lumpy Skin Disease ◽  
Herd Level ◽  
Level Information

Lumpy skin disease (LSD) is an acute viral disease infectious of cattle and recently emerged very common in Bangladesh causing economic losses. Hence, this study was design to investigate the prevalence of LSD in considering the herd level and some of management status. Thus, a total of 453 sick animals were subjected to study during the period of April 2020 to July 2020 in Dinajpur. LSD was confirmed according to the clinical inspection and microscopic study of skin scraping. The results indicated that the overall prevalence of LSD was 41.06% in cattle. Moreover, the local breed (75%) and young cattle less than one year (64%) were significantly (p<0.001) higher for LSD with the significant (p<0.001) skin lesions in whole body (44%). In addition, the animal grazed in flock (61%), non-dewormed (58%), non-vaccinated (61%) was significantly (p<0.05) higher for LSD. In the same way, 75% prevalence was in without fly repellent (p<0.001). Afterward, the univariate logistic regression in herd level information had the odd ratio of local breed (95% CI: 0.244-0.553), skin lesions in abdominal regions (95% CI: 1.620-5.923) and pregnant cattle (95% CI: 1.057-3.386) was 0.367, 3.098 and 1.892 respectively indicated the likelihood of no LSD outbreaks. Besides this, the odd ratio of dewormed cattle, vaccinated, individually grazed, regular use of disinfectant and fly repellent farm animal was 1.493 (95% CI:1.024-2.177), 1.491 (95% CI:1.020-2.180), 1.656 (95% CI:1.133-2.421), 1.516 (95% CI:0.952-2.414) and 1.660 (95% CI:1.097-2.513), respectively indicated the likelihood of no LSD. Therefore, LSD infection can be greatly reduced by practicing regular vaccination, deworming, and disinfection, vector controlling and allowing grazing individually, especially with great concern to young female cattle of local breed. Res. Agric., Livest. Fish.7(3): 445-455,  December 2020

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