AIMC Topic: Skin Neoplasms
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Robust ROI Detection in Whole Slide Images Guided by Pathologists' Viewing Patterns.
Journal of imaging informatics in medicine
Aug 9, 2024
Deep learning techniques offer improvements in computer-aided diagnosis systems. However, acquiring image domain annotations is challenging due to the knowledge and commitment required of expert pathologists. Pathologists often identify regions in wh...
The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation.
International journal of dermatology
Aug 9, 2024
BACKGROUND: Artificial intelligence (AI) and large language models (LLMs) transform how patients inform themselves. LLMs offer potential as educational tools, but their quality depends upon the information generated. Current literature examining AI a...
Artificial intelligence-assisted metastasis and prognosis model for patients with nodular melanoma.
PloS one
Aug 7, 2024
OBJECTIVE: The objective of this study was to identify the risk factors that influence metastasis and prognosis in patients with nodular melanoma (NM), as well as to develop and validate a prognostic model using artificial intelligence (AI) algorithm...
Utilizing deep learning model for assessing melanocytic density in resection margins of lentigo maligna.
Diagnostic pathology
Aug 3, 2024
BACKGROUND: Surgical excision with clear histopathological margins is the preferred treatment to prevent progression of lentigo maligna (LM) to invasive melanoma. However, the assessment of resection margins on sun-damaged skin is challenging. We dev...
Adaptive neighborhood triplet loss: enhanced segmentation of dermoscopy datasets by mining pixel information.
International journal of computer assisted radiology and surgery
Aug 2, 2024
PURPOSE: The integration of deep learning in image segmentation technology markedly improves the automation capabilities of medical diagnostic systems, reducing the dependence on the clinical expertise of medical professionals. However, the accuracy ...
Enhanced skin cancer diagnosis using optimized CNN architecture and checkpoints for automated dermatological lesion classification.
BMC medical imaging
Aug 2, 2024
Skin cancer stands as one of the foremost challenges in oncology, with its early detection being crucial for successful treatment outcomes. Traditional diagnostic methods depend on dermatologist expertise, creating a need for more reliable, automated...
Skin cancer detection through attention guided dual autoencoder approach with extreme learning machine.
Scientific reports
Aug 1, 2024
Skin cancer is a lethal disease, and its early detection plays a pivotal role in preventing its spread to other body organs and tissues. Artificial Intelligence (AI)-based automated methods can play a significant role in its early detection. This stu...
Using deep learning to decipher the impact of telomerase promoter mutations on the dynamic metastatic morpholome.
PLoS computational biology
Jul 30, 2024
Melanoma showcases a complex interplay of genetic alterations and intra- and inter-cellular morphological changes during metastatic transformation. While pivotal, the role of specific mutations in dictating these changes still needs to be fully eluci...
Skin cancer classification based on an optimized convolutional neural network and multicriteria decision-making.
Scientific reports
Jul 27, 2024
Skin cancer is a type of cancer disease in which abnormal alterations in skin characteristics can be detected. It can be treated if it is detected early. Many artificial intelligence-based models have been developed for skin cancer detection and clas...
Deep learning-assisted multispectral imaging for early screening of skin diseases.
Photodiagnosis and photodynamic therapy
Jul 26, 2024
INTRODUCTION: Melanocytic nevi (MN), warts, seborrheic keratoses (SK), and psoriasis are four common types of skin surface lesions that typically require dermatoscopic examination for definitive diagnosis in clinical dermatology settings. This proces...
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