The code is available at https://github.com/catarina-barata/CBIR_Explainability_Skin_Cancer .Barata, CatarinaInstituto Superior TécnicoSantiago, CarlosInstituto Superior TécnicoSpringer, Cham
The de-identified teledermatology data used in this study are not publicly available due to restrictions in the data-sharing agreement. Code availability The deep learning framework (TensorFlow) used in this study is available athttps://www.tensorflow.org/. The training framework (Estimator) is av...
Although advances in deep learning systems for image-based medical diagnosis demonstrate their potential to augment clinical decision-making, the effectiveness of physician–machine partnerships remains an open question, in part because physicians and al
Medical diagnosis refers to the process of identifying the cause of a patient's illness or condition by analyzing information obtained from various sources such as physical examination, lab tests, and patient medical records. It is a crucial step in determining the most effective treatment for the...
15 to learn the feature representations of skin diseases in an unsupervised manner using the online clustering method. SwAV encodes two different augmented views of the same image into features zt and zs respectively. Then a set of trainable code vectors qt and qs are computed by matching these...
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Skin cancer detection is a project that aims to detect skin cancer through digital image analysis. The system uses machine learning algorithms to analyze images of skin lesions and determine if they are cancerous or not. This project can be helpful for early diagnosis of skin cancer, which can...
Results shown here for the two datasets setting when the observations are mix of history, signs, symptoms and labs. We include training and testing configurations that include and exclude lab results. See Tbl. 4 for a more detailed breakdown based on the types of input observations to the ...
The development of diagnostic tools for skin cancer based on artificial intelligence (AI) is increasing rapidly and will likely soon be widely implemented in clinical use. Even though the performance of these algorithms is promising in theory, there is limited evidence on the impact of AI assistanc...
Large language models (LLMs) are seen to have tremendous potential in advancing medical diagnosis recently, particularly in dermatological diagnosis, which is a very important task as skin and subcutaneous diseases rank high among the leading contributors to the global burden of nonfatal diseases. Her...