https://DAISYlabs.github.io/tags/skin-imaging/Skin ImagingSource Themes Academic (https://sourcethemes.com/academic/)en-us©2018–2021 DAISYlabs · ArchivedFri, 30 Aug 2019 00:00:00 +0000https://DAISYlabs.github.io/images/logo_hu_bf6817d0bc4ba410.pnghttps://DAISYlabs.github.io/tags/skin-imaging/https://DAISYlabs.github.io/project/2019-isic-task2/Fri, 30 Aug 2019 00:00:00 +0000https://DAISYlabs.github.io/project/2019-isic-task2/<h1 id="aim">Aim</h1> <p>The goal for ISIC 2019: Task 1 is classify dermoscopic images with additional available meta-data (e.g. age, anatomical site, and sex) among nine different diagnostic categories.</p> <h1 id="background">Background</h1> <p>Skin cancer is a major public health problem, with over 5,000,000 newly diagnosed cases in the United States every year. Melanoma is the deadliest form of skin cancer, responsible for an overwhelming majority of skin cancer deaths. In 2015, the global incidence of melanoma was estimated to be over 350,000 cases, with almost 60,000 deaths. Although the mortality is significant, when detected early, melanoma survival exceeds 95%.</p> <h2 id="about-dermoscopy">About Dermoscopy</h2> <p>As pigmented lesions occurring on the surface of the skin, melanoma is amenable to early detection by expert visual inspection. It is also amenable to automated detection with image analysis. Given the widespread availability of high-resolution cameras, algorithms that can improve our ability to screen and detect troublesome lesions can be of great value. As a result, many centers have begun their own research efforts on automated analysis. However, a centralized, coordinated, and comparative effort across institutions has yet to be implemented. Dermoscopy is an imaging technique that eliminates the surface reflection of skin. By removing surface reflection, visualization of deeper levels of skin is enhanced. Prior research has shown that when used by expert dermatologists, dermoscopy provides improved diagnostic accuracy, in comparison to standard photography. As inexpensive consumer dermatoscope attachments for smart phones are beginning to reach the market, the opportunity for automated dermoscopic assessment algorithms to positively influence patient care increases.</p> <h1 id="results">Results</h1> <p><img src="https://DAISYlabs.github.io/project/2019-isic-task2/results.png" alt="alt text"></p> <h2 id="challenge">Challenge</h2> <p> <a href="https://challenge2019.isic-archive.com/" target="_blank" rel="noopener">https://challenge2019.isic-archive.com/</a></p>https://DAISYlabs.github.io/project/2019-isic-task1/Fri, 16 Aug 2019 00:00:00 +0000https://DAISYlabs.github.io/project/2019-isic-task1/<h1 id="aim">Aim</h1> <p>The goal for ISIC 2019: Task 1 is classify dermoscopic images without meta-data among nine different diagnostic categories:</p> <ol> <li>Melanoma</li> <li>Melanocytic nevus</li> <li>Basal cell carcinoma</li> <li>Actinic keratosis</li> <li>Benign keratosis (solar lentigo / seborrheic keratosis / lichen planus-like keratosis)</li> <li>Dermatofibroma</li> <li>Vascular lesion</li> <li>Squamous cell carcinoma</li> <li>None of the others</li> </ol> <h1 id="background">Background</h1> <p>Skin cancer is a major public health problem, with over 5,000,000 newly diagnosed cases in the United States every year. Melanoma is the deadliest form of skin cancer, responsible for an overwhelming majority of skin cancer deaths. In 2015, the global incidence of melanoma was estimated to be over 350,000 cases, with almost 60,000 deaths. Although the mortality is significant, when detected early, melanoma survival exceeds 95%.</p> <h2 id="about-dermoscopy">About Dermoscopy</h2> <p>As pigmented lesions occurring on the surface of the skin, melanoma is amenable to early detection by expert visual inspection. It is also amenable to automated detection with image analysis. Given the widespread availability of high-resolution cameras, algorithms that can improve our ability to screen and detect troublesome lesions can be of great value. As a result, many centers have begun their own research efforts on automated analysis. However, a centralized, coordinated, and comparative effort across institutions has yet to be implemented. Dermoscopy is an imaging technique that eliminates the surface reflection of skin. By removing surface reflection, visualization of deeper levels of skin is enhanced. Prior research has shown that when used by expert dermatologists, dermoscopy provides improved diagnostic accuracy, in comparison to standard photography. As inexpensive consumer dermatoscope attachments for smart phones are beginning to reach the market, the opportunity for automated dermoscopic assessment algorithms to positively influence patient care increases.</p> <h1 id="results">Results</h1> <p><img src="https://DAISYlabs.github.io/project/2019-isic-task1/results.png" alt="alt text"></p> <h2 id="challenge">Challenge</h2> <p> <a href="https://challenge2019.isic-archive.com/" target="_blank" rel="noopener">https://challenge2019.isic-archive.com/</a></p>https://DAISYlabs.github.io/project/2018-isic-task3/Fri, 27 Apr 2018 00:00:00 +0000https://DAISYlabs.github.io/project/2018-isic-task3/<h1 id="aim">Aim</h1> <p>Submit automated predictions of disease classification within dermoscopic images. Possible disease categories are:</p> <ol> <li>Melanoma</li> <li>Melanocytic nevus</li> <li>Basal cell carcinoma</li> <li>Actinic keratosis / Bowen’s disease (intraepithelial carcinoma)</li> <li>Benign keratosis (solar lentigo / seborrheic keratosis / lichen planus-like keratosis)</li> <li>Dermatofibroma</li> <li>Vascular lesion</li> </ol> <h1 id="background">Background</h1> <p>Skin cancer is the most common cancer globally, with melanoma being the most deadly form. Dermoscopy is a skin imaging modality that has demonstrated improvement for diagnosis of skin cancer compared to unaided visual inspection. However, clinicians should receive adequate training for those improvements to be realized. In order to make expertise more widely available, the International Skin Imaging Collaboration (ISIC) has developed the ISIC Archive, an international repository of dermoscopic images, for both the purposes of clinical training, and for supporting technical research toward automated algorithmic analysis by hosting the ISIC Challenges.</p> <h1 id="results">Results</h1> <p><img src="https://DAISYlabs.github.io/project/2018-isic-task3/results.png" alt="alt text"></p> <h2 id="challenge">Challenge</h2> <p> <a href="https://challenge2018.isic-archive.com" target="_blank" rel="noopener">https://challenge2018.isic-archive.com</a></p>https://DAISYlabs.github.io/project/2018-isic-task1/Thu, 26 Apr 2018 00:00:00 +0000https://DAISYlabs.github.io/project/2018-isic-task1/<h1 id="aim">Aim</h1> <p>Submit automated predictions of lesion segmentation boundaries within dermoscopic images.</p> <h1 id="background">Background</h1> <p>Skin cancer is the most common cancer globally, with melanoma being the most deadly form. Dermoscopy is a skin imaging modality that has demonstrated improvement for diagnosis of skin cancer compared to unaided visual inspection. However, clinicians should receive adequate training for those improvements to be realized. In order to make expertise more widely available, the International Skin Imaging Collaboration (ISIC) has developed the ISIC Archive, an international repository of dermoscopic images, for both the purposes of clinical training, and for supporting technical research toward automated algorithmic analysis by hosting the ISIC Challenges.</p> <h1 id="results">Results</h1> <p><img src="https://DAISYlabs.github.io/project/2018-isic-task1/results.png" alt="alt text"></p> <h2 id="challenge">Challenge</h2> <p> <a href="https://challenge2018.isic-archive.com" target="_blank" rel="noopener">https://challenge2018.isic-archive.com</a></p>