| 1. |
- Annala, Leevi, et al.
(författare)
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Generating Hyperspectral Skin Cancer Imagery using Generative Adversarial Neural Network.
- 2020
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Ingår i: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. - 2694-0604. ; 2020, s. 1600-1603
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Tidskriftsartikel (refereegranskat)abstract
- In this study we develop a proof of concept of using generative adversarial neural networks in hyperspectral skin cancer imagery production. Generative adversarial neural network is a neural network, where two neural networks compete. The generator tries to produce data that is similar to the measured data, and the discriminator tries to correctly classify the data as fake or real. This is a reinforcement learning model, where both models get reinforcement based on their performance. In the training of the discriminator we use data measured from skin cancer patients. The aim for the study is to develop a generator for augmenting hyperspectral skin cancer imagery.
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| 2. |
- Bieliauskiene, Giedre, et al.
(författare)
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Incidence of Kaposi Sarcoma in Sweden is Decreasing.
- 2020
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Ingår i: Acta dermato-venereologica. - : Medical Journals Sweden AB. - 1651-2057. ; 100:17
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Tidskriftsartikel (refereegranskat)abstract
- Kaposi sarcoma is a rare skin cancer, and epidemiological research into Kaposi sarcoma is therefore scarce. The current epidemiological situation for Kaposi sarcoma in Sweden is unknown. The authors hypothesized that the incidence of Kaposi sarcoma should have decreased after the introduction of antiretroviral therapy in 1996. Using data from the Swedish Cancer Registry, this study aimed to determine the incidence rates and survival for Kaposi sarcoma in Sweden from 1993 to 2016. The results showed that a total of 657 patients (74.0% men, 26.0% women) were diagnosed with Kaposi sarcoma in Sweden during 1993 to 2016. The overall incidence per 100,000, age-standardized to the world population, decreased from 0.40 to 0.10 (p=0.003) for both sexes combined, from 0.76 to 0.14 (p=0.003) for men, and from 0.07 to 0.06 (p=0.86) for women. The 10-year overall survival rate was significantly lower for the study population (30%) compared with the age- and sex-matched Swedish population (56%) (p<0.00001). Over the study period, incidence rates of Kaposi sarcoma decreased significantly in men, especially during the late 1990s.
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| 3. |
- Nilsson, Kelly Dimovska, et al.
(författare)
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TOF-SIMS imaging reveals tumor heterogeneity and inflammatory response markers in the microenvironment of basal cell carcinoma
- 2020
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Ingår i: Biointerphases. - : American Vacuum Society. - 1559-4106 .- 1934-8630. ; 15:4
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Tidskriftsartikel (refereegranskat)abstract
- Basal cell carcinoma (BCC) is the most common skin malignancy. In fact, it is as common as the sum of all other skin malignancies combined and the incidence is rising. In this focused and histology-guided study, tissue from a patient diagnosed with aggressive BCC was analyzed by imaging mass spectrometry in order to probe the chemistry of the complex tumor environment. Time-of-flight secondary ion mass spectrometry using a (CO2)(6 k)(+)gas cluster ion beam allowed a wide range of lipid species to be detected. Their distributions were then imaged in the tissue that contained small tumor islands that were histologically classified as more/less aggressive. Maximum autocorrelation factor (MAF) analysis highlighted chemical differences between the tumors and the surrounding stroma. A closer inspection of the distribution of individual ions, selected based on the MAF loadings, showed heterogeneity in signal between different microtumors, suggesting the potential of chemically grading the aggressiveness of each individual tumor island. Sphingomyelin lipids were found to be located in stroma containing inflammatory cells.
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| 4. |
- Zaar, Oscar, et al.
(författare)
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Evaluation of the Diagnostic Accuracy of an Online Artificial Intelligence Application for Skin Disease Diagnosis.
- 2020
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Ingår i: Acta dermato-venereologica. - : Medical Journals Sweden AB. - 0001-5555 .- 1651-2057. ; 100:16
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Tidskriftsartikel (refereegranskat)abstract
- Artificial intelligence (AI) algorithms for automated classification of skin diseases are available to the consumer market. Studies of their diagnostic accuracy are rare. We assessed the diagnostic accuracy of an open-access AI application (Skin Image Search™) for recognition of skin diseases. Clinical images including tumours, infective and inflammatory skin diseases were collected at the Department of Dermatology at the Sahlgrenska University Hospital and uploaded for classification by the online application. The AI algorithm classified the images giving 5 differential diagnoses, which were then compared to the diagnoses made clinically by the dermatologists and/or histologically. We included 521 images portraying 26 diagnoses. The diagnostic accuracy was 56.4% for the top 5 suggested diagnoses and 22.8% when only considering the most probable diagnosis. The level of diagnostic accuracy varied considerably for diagnostic groups. The online application demonstrated low diagnostic accuracy compared to a dermatologist evaluation and needs further development.
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