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相关论文: AI Progress in Skin Lesion Analysis

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Dermatological conditions affect 1.9 billion people globally, yet accurate diagnosis remains challenging due to limited specialist availability and complex clinical presentations. Family history significantly influences skin disease…

计算机视觉与模式识别 · 计算机科学 2026-03-03 Satya Narayana Panda , Vaishnavi Kukkala , Spandana Iyer

Skin diseases, such as skin cancer, are a significant public health issue, and early diagnosis is crucial for effective treatment. Artificial intelligence (AI) algorithms have the potential to assist in triaging benign vs malignant skin…

计算机视觉与模式识别 · 计算机科学 2025-03-25 Nusrat Munia , Abdullah-Al-Zubaer Imran

in healthcare. However, the existing AI model may be biased in its decision marking. The bias induced by data itself, such as collecting data in subgroups only, can be mitigated by including more diversified data. Distributed and…

分布式、并行与集群计算 · 计算机科学 2021-09-28 Di Fan , Yifan Wu , Xiaoxiao Li

Skin lesion datasets consist predominantly of normal samples with only a small percentage of abnormal ones, giving rise to the class imbalance problem. Also, skin lesion images are largely similar in overall appearance owing to the low…

图像与视频处理 · 电气工程与系统科学 2020-07-29 Hasib Zunair , A. Ben Hamza

Skin cancer can be identified by dermoscopic examination and ocular inspection, but early detection significantly increases survival chances. Artificial intelligence (AI), using annotated skin images and Convolutional Neural Networks…

计算机视觉与模式识别 · 计算机科学 2025-12-24 Abdullah Al Shafi , Abdul Muntakim , Pintu Chandra Shill , Rowzatul Zannat , Abdullah Al-Amin

Early detection of malignant skin lesions is critical for improving patient outcomes in aggressive, metastatic skin cancers. This study evaluates a comprehensive system for preliminary skin lesion assessment that combines the clinically…

图像与视频处理 · 电气工程与系统科学 2026-01-23 Ali Khreis , Ro'Yah Radaideh , Quinn McGill

Skin lesions segmentation is an important step in the process of automated diagnosis of the skin melanoma. However, the accuracy of segmenting melanomas skin lesions is quite a challenging task due to less data for training, irregular…

图像与视频处理 · 电气工程与系统科学 2020-12-29 Sabari Nathan , Priya Kansal

Dry eye disease (DED) has a prevalence of between 5 and 50\%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests…

Automated skin lesion analysis is very crucial in clinical practice, as skin cancer is among the most common human malignancy. Existing approaches with deep learning have achieved remarkable performance on this challenging task, however,…

计算机视觉与模式识别 · 计算机科学 2019-09-06 Xueying Shi , Qi Dou , Cheng Xue , Jing Qin , Hao Chen , Pheng-Ann Heng

According to PBS, nearly one-third of Americans lack access to primary care services, and another forty percent delay going to avoid medical costs. As a result, many diseases are left undiagnosed and untreated, even if the disease shows…

图像与视频处理 · 电气工程与系统科学 2024-11-22 Allen Yang , Edward Yang

The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion analysis is an emerging field of research that has the potential to alleviate the burden and cost of skin cancer screening. Researchers have recently indicated…

图像与视频处理 · 电气工程与系统科学 2023-02-03 Md. Kamrul Hasan , Md. Asif Ahamad , Choon Hwai Yap , Guang Yang

Facial analysis has emerged as a prominent area of research with diverse applications, including cosmetic surgery programs, the beauty industry, photography, and entertainment. Manipulating patient images often necessitates professional…

图像与视频处理 · 电气工程与系统科学 2024-02-14 Reza Sarshar , Mohammad Heydari , Elham Akhondzadeh Noughabi

Automatic skin lesion segmentation on dermoscopic images is an essential step in computer-aided diagnosis of melanoma. However, this task is challenging due to significant variations of lesion appearances across different patients. This…

计算机视觉与模式识别 · 计算机科学 2017-09-29 Yading Yuan , Yeh-Chi Lo

As many other machine learning driven medical image analysis tasks, skin image analysis suffers from a chronic lack of labeled data and skewed class distributions, which poses problems for the training of robust and well-generalizing…

计算机视觉与模式识别 · 计算机科学 2018-09-07 Christoph Baur , Shadi Albarqouni , Nassir Navab

This article describes the design, implementation, and results of the latest installment of the dermoscopic image analysis benchmark challenge. The goal is to support research and development of algorithms for automated diagnosis of…

Skin lesions are conditions that appear on a patient due to many different reasons. One of these can be because of an abnormal growth in skin tissue, defined as cancer. This disease plagues more than 14.1 million patients and had been the…

计算机视觉与模式识别 · 计算机科学 2018-12-07 Danilo Barros Mendes , Nilton Correia da Silva

Skin cancer, particularly melanoma, remains a major cause of morbidity and mortality, making early detection critical. AI-driven dermatology systems often rely on skin lesion segmentation as a preprocessing step to delineate the lesion from…

计算机视觉与模式识别 · 计算机科学 2026-04-01 Kuniko Paxton , Medina Kapo , Amila Akagić , Koorosh Aslansefat , Dhavalkumar Thakker , Yiannis Papadopoulos

Skin conditions are a global health concern, ranking the fourth highest cause of nonfatal disease burden when measured as years lost due to disability. As diagnosing, or classifying, skin diseases can help determine effective treatment,…

计算机视觉与模式识别 · 计算机科学 2019-06-05 Jeremy Kawahara , Ghassan Hamarneh

This work summarizes the results of the largest skin image analysis challenge in the world, hosted by the International Skin Imaging Collaboration (ISIC), a global partnership that has organized the world's largest public repository of…

Skin cancer is a major public health problem around the world. Its early detection is very important to increase patient prognostics. However, the lack of qualified professionals and medical instruments are significant issues in this field.…

图像与视频处理 · 电气工程与系统科学 2019-12-09 Andre G. C. Pacheco , Renato A. Krohling