AIMC Topic: Dermoscopy

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LAMA: Lesion-Aware Mixup Augmentation for Skin Lesion Segmentation.

Journal of imaging informatics in medicine
Deep learning can exceed dermatologists' diagnostic accuracy in experimental image environments. However, inaccurate segmentation of images with multiple skin lesions can be seen with current methods. Thus, information present in multiple-lesion imag...

Basal Cell Carcinoma Diagnosis with Fusion of Deep Learning and Telangiectasia Features.

Journal of imaging informatics in medicine
In recent years, deep learning (DL) has been used extensively and successfully to diagnose different cancers in dermoscopic images. However, most approaches lack clinical inputs supported by dermatologists that could aid in higher accuracy and explai...

Fusion between an Algorithm Based on the Characterization of Melanocytic Lesions' Asymmetry with an Ensemble of Convolutional Neural Networks for Melanoma Detection.

The Journal of investigative dermatology
Melanoma is still a major health problem worldwide. Early diagnosis is the first step toward reducing its mortality, but it remains a challenge even for experienced dermatologists. Although computer-aided systems have been developed to help diagnosis...

Understanding skin color bias in deep learning-based skin lesion segmentation.

Computer methods and programs in biomedicine
BACKGROUND: The field of dermatological image analysis using deep neural networks includes the semantic segmentation of skin lesions, pivotal for lesion analysis, pathology inference, and diagnoses. While biases in neural network-based dermatoscopic ...

Artificial intelligence in the detection of skin cancer: State of the art.

Clinics in dermatology
The incidence of melanoma is increasing rapidly. This cancer has a good prognosis if detected early. For this reason, various systems of skin lesion image analysis, which support imaging diagnostics of this neoplasm, are developing very dynamically. ...

Consistency of convolutional neural networks in dermoscopic melanoma recognition: A prospective real-world study about the pitfalls of augmented intelligence.

Journal of the European Academy of Dermatology and Venereology : JEADV
BACKGROUND: Deep-learning convolutional neural networks (CNNs) have outperformed even experienced dermatologists in dermoscopic melanoma detection under controlled conditions. It remains unexplored how real-world dermoscopic image transformations aff...

Auditing the inference processes of medical-image classifiers by leveraging generative AI and the expertise of physicians.

Nature biomedical engineering
The inferences of most machine-learning models powering medical artificial intelligence are difficult to interpret. Here we report a general framework for model auditing that combines insights from medical experts with a highly expressive form of exp...

SkinViT: A transformer based method for Melanoma and Nonmelanoma classification.

PloS one
Over the past few decades, skin cancer has emerged as a major global health concern. The efficacy of skin cancer treatment greatly depends upon early diagnosis and effective treatment. The automated classification of Melanoma and Nonmelanoma is quite...

Integrated convolutional neural network for skin cancer classification with hair and noise restoration.

Turkish journal of medical sciences
BACKGROUND/AIM: Skin lesions are commonly diagnosed and classified using dermoscopic images. There are many artifacts visible in dermoscopic images, including hair strands, noise, bubbles, blood vessels, poor illumination, and moles. These artifacts ...