scholarly journals Automated Bias Reduction in Deep Learning Based Melanoma Diagnosis using a Semi-Supervised Algorithm

2021 ◽  
Author(s):  
Sauman Das
Keyword(s):  
Deep Learning ◽  
Skin Color ◽  
Racial Bias ◽  
Skin Lesions ◽  
Bias Reduction ◽  
Type I ◽  
Melanoma Diagnosis ◽  
Light Skin ◽  
Common Skin ◽  
Significant Performance

AbstractMelanoma is one of the most fatal forms of skin cancer and is often very difficult to differentiate from other benign skin lesions. However, if detected at its early stages, it can almost always be cured. Researchers and data scientists have studied this disease in-depth with the help of large datasets containing high-quality dermascopic images, such as those assembled by the International Skin Imaging Collaboration (ISIC). However, these images often lack diversity and over-represent patients with very common skin features such as light skin and having no visible body hair. In this study, we introduce a novel architecture called LatentNet which automatically detects over-represented features and reduces their weights during training. We tested our model on four distinct categories - three skin color levels corresponding to Type I, II, and III on the Fitzpatrick Scale, and images containing visible hair. We then compared the accuracy against the conventional Deep Convolutional Neural Network (DCNN) model trained using the standard approach (i.e. without detecting over-represented features) and containing the same hyper-parameters as the LatentNet. LatentNet showed significant performance improvement over the standard DCNN model with accuracy of 89.52%, 79.05%, 64.31%, and 64.35% compared to the DCNN accuracy of 90.41%, 70.82%, 45.28%, 56.52% in the corresponding categories, respectively. Differences in the average performance between the models were statistically significant (p < 0.05), suggesting that the proposed model successfully reduced bias amongst the tested categories. LatentNet is the first architecture that addresses racial bias (and other sources of bias) in deep-learning based Melanoma diagnosis.

Deep‐learning approach in the study of skin lesions

2021 ◽  
Author(s):  
Stefan‐Gabriel Filipescu ◽  
Alexandra‐Irina Butacu ◽  
George‐Sorin Tiplica ◽  
Dumitru‐Iulian Nastac
Keyword(s):  
Deep Learning ◽  
Skin Lesions ◽  
Learning Approach

Molecular pathogenesis and clinical implications of eczema herpeticum

2008 ◽  
Vol 10 ◽  
Author(s):  
Caroline Bussmann ◽  
Wen-Ming Peng ◽  
Thomas Bieber ◽  
Natalija Novak
Keyword(s):  
Atopic Dermatitis ◽  
Age Of Onset ◽  
Current Knowledge ◽  
Allergen Challenge ◽  
Skin Lesions ◽  
Molecular Pathogenesis ◽  
Type I Interferons ◽  
Total Serum ◽  
Type I ◽  
Eczema Herpeticum

A subgroup of patients with atopic dermatitis develops one or more episodes of a severe viral skin infection caused by herpes simplex virus superimposed on eczematous skin lesions. This condition is named atopic dermatitis complicated by eczema herpeticum. Characteristic features of patients developing eczema herpeticum include an early age of onset of atopic dermatitis with a persistent and severe course into adulthood, predilection for eczematous skin lesions in the head and neck area, elevated total serum IgE levels and increased allergen sensitisation. Deficiencies at the level of both the innate and the adaptive immune system, which have been identified in atopic dermatitis, are much more pronounced in this subgroup. Predisposing cellular factors include a reduced number of plasmacytoid dendritic cells in the epidermis and a modified capacity of these cells to produce type I interferons after allergen challenge. In addition, lower levels of antimicrobial peptides in the skin of atopic dermatitis patients, resulting in part from a Th2-prone micromilieu, contribute to the lack of an effective defence against viral attack. In this review, we summarise the current knowledge of the molecular pathogenesis of eczema herpeticum.

“Because I’m Light Skin . . . They Think I’m Italian”

2016 ◽  
Vol 52 (4) ◽  
pp. 460-490 ◽  
Author(s):  
Edward Fergus
Keyword(s):  
Skin Color ◽  
Color Variation ◽  
Ethnic Identification ◽  
Opportunity Structure ◽  
Mexican Students ◽  
Light Skin ◽  
Racial Ethnic

Discussions on Latino/a students’ interpretation of the opportunity structure and schooling treat racial/ethnic identification among Latino/as as static, despite skin color variation. This article provides findings from interviews with six Mexican students who discussed teachers identifying them as “White-looking” or “Hispanic/Mexican-looking.” Both groups shared belief in the achievement ideology and understood the opportunity structure as fraught with barriers. However, the “White-looking” students perceived themselves as being able to permeate such barriers meanwhile the “Hispanic/Mexican-looking” students believed such barriers affect their ability to “make it” regardless of their aspirations. This study raises questions regarding theories on academic variability of Latino/a students.

scholarly journals A Test of Racial Bias in Capital Sentencing

2014 ◽  
Vol 104 (11) ◽  
pp. 3397-3433 ◽  
Author(s):  
Alberto Alesina ◽  
Eliana La Ferrara
Keyword(s):  
United States ◽  
Capital Punishment ◽  
Racial Bias ◽  
The United States ◽  
Type I ◽  
Habeas Corpus ◽  
Trial Courts ◽  
Southern States ◽  
Percentage Points ◽  
White Defendants

We collect a new dataset on capital punishment in the United States and we propose a test of racial bias based upon patterns of sentence reversals. We model the courts as minimizing type I and II errors. If trial courts were unbiased, conditional on defendant's race the error rate should be independent of the victim's race. Instead we uncover 3 and 9 percentage points higher reversal rates in direct appeal and habeas corpus cases, respectively, against minority defendants who killed whites. The pattern for white defendants is opposite but not statistically significant. This bias is confined to Southern states. (JEL J15, K41, K42)

Human Skin Detection in Color Images Using Deep Learning

2015 ◽  
Vol 5 (2) ◽  
pp. 1-13 ◽  
Author(s):  
Mohammadreza Hajiarbabi ◽  
Arvin Agah
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Human Skin ◽  
Skin Color ◽  
Color Image ◽  
Gaussian Model ◽  
Color Images ◽  
Skin Detection ◽  
Learning Methods ◽  
Rule Based

Human skin detection is an important and challenging problem in computer vision. Skin detection can be used as the first phase in face detection when using color images. The differences in illumination and ranges of skin colors have made skin detection a challenging task. Gaussian model, rule based methods, and artificial neural networks are methods that have been used for human skin color detection. Deep learning methods are new techniques in learning that have shown improved classification power compared to neural networks. In this paper the authors use deep learning methods in order to enhance the capabilities of skin detection algorithms. Several experiments have been performed using auto encoders and different color spaces. The proposed technique is evaluated compare with other available methods in this domain using two color image databases. The results show that skin detection utilizing deep learning has better results compared to other methods such as rule-based, Gaussian model and feed forward neural network.

Fusion of Visual and Anamnestic Data for the Classification of Skin Lesions with Deep Learning

2019 ◽  
pp. 211-219
Author(s):  
Simone Bonechi ◽  
Monica Bianchini ◽  
Pietro Bongini ◽  
Giorgio Ciano ◽  
Giorgia Giacomini ◽  
...  
Keyword(s):  
Deep Learning ◽  
Skin Lesions

scholarly journals Establishing exhaustive metasurface robustness against fabrication uncertainties through deep learning

Nanophotonics ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ronald P. Jenkins ◽  
Sawyer D. Campbell ◽  
Douglas H. Werner
Keyword(s):  
Deep Learning ◽  
Design Process ◽  
Functional Materials ◽  
Computational Electromagnetics ◽  
Inverse Design ◽  
Design Tools ◽  
Structural Error ◽  
Significant Performance ◽  
Engineered Materials ◽  
Exhaustive Study

Abstract Photonic engineered materials have benefitted in recent years from exciting developments in computational electromagnetics and inverse-design tools. However, a commonly encountered issue is that highly performant and structurally complex functional materials found through inverse-design can lose significant performance upon being fabricated. This work introduces a method using deep learning (DL) to exhaustively analyze how structural issues affect the robustness of metasurface supercells, and we show how systems can be designed to guarantee significantly better performance. Moreover, we show that an exhaustive study of structural error is required to make strong guarantees about the performance of engineered materials. The introduction of DL into the inverse-design process makes this problem tractable, enabling optimization runtimes to be measurable in days rather than months and allowing designers to establish exhaustive metasurface robustness guarantees.

scholarly journals A Rare Case of Congenital Methemoglobinemia with secondary polycythemia- Case Report and Literature Review

2019 ◽  
Vol 3 (01) ◽  
pp. 20-23
Author(s):  
Tasneem Ara ◽  
Qazi Smita Haque ◽  
Salma Afrose
Keyword(s):  
Skin Color ◽  
Rare Case ◽  
Heart Diseases ◽  
Final Diagnosis ◽  
Enzyme Deficiency ◽  
Type I ◽  
Methemoglobin Level ◽  
Hands And Feet ◽  
First Time

Congenital heart diseases are common cause of congenital cyanosis with polycythaemia. Congenital methemoglobinemia is a rare cause of lifelong cyanosis with polycythemia. Congenital methemoglobinemia is caused either by enzyme deficiency or by an abnormal Hb (Hb M). Asymptomatic despite presence of severe cyanosis indicates this rare disorder. We are reporting a rare case of polycythemia with cyanosis due to congenital methemoglobinemia. The patient was referred to our centre and attended Hematology OPD (out-patient department) when his routine CBC revealed erythrocytosis. At that time, we found him severely cyanosed especially apparent on lips, tongue, hands and feet. He was diagnosed as a case of congenital methemoglobinemia with 38% blood methemoglobin level (normal value-0.00-2.00%). On view of life long persistent cyanosis, without any cardiopulmonary and neurological abnormality, consanguinity of parent’s marriage, dark colored blood with high methemoglobin level, a final diagnosis of Type I enzyme deficiency congenital methemoglobinemia was made. He was treated with oral ascorbic acid 250 mg twice daily. At follow up after 6 months his skin color improved and RBC count returned to normal. We are reporting this case of congenital methemoglobinemia for the first time in Bangladesh to emphasize the importance of this rare entity in the differential diagnosis of asymptomatic cyanosis with polycythemia.

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