[HTML][HTML] Transparency of deep neural networks for medical image analysis: A review of interpretability methods
Artificial Intelligence (AI) has emerged as a useful aid in numerous clinical applications for
diagnosis and treatment decisions. Deep neural networks have shown the same or better …
diagnosis and treatment decisions. Deep neural networks have shown the same or better …
Lack of transparency and potential bias in artificial intelligence data sets and algorithms: a scoping review
R Daneshjou, MP Smith, MD Sun… - JAMA …, 2021 - jamanetwork.com
Importance Clinical artificial intelligence (AI) algorithms have the potential to improve clinical
care, but fair, generalizable algorithms depend on the clinical data on which they are trained …
care, but fair, generalizable algorithms depend on the clinical data on which they are trained …
[HTML][HTML] Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions
Understanding black box models has become paramount as systems based on opaque
Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response …
Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response …
[HTML][HTML] A patient-centric dataset of images and metadata for identifying melanomas using clinical context
Prior skin image datasets have not addressed patient-level information obtained from
multiple skin lesions from the same patient. Though artificial intelligence classification …
multiple skin lesions from the same patient. Though artificial intelligence classification …
[HTML][HTML] Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts
Background Multiple studies have compared the performance of artificial intelligence (AI)–
based models for automated skin cancer classification to human experts, thus setting the …
based models for automated skin cancer classification to human experts, thus setting the …
[HTML][HTML] Human–computer collaboration for skin cancer recognition
The rapid increase in telemedicine coupled with recent advances in diagnostic artificial
intelligence (AI) create the imperative to consider the opportunities and risks of inserting AI …
intelligence (AI) create the imperative to consider the opportunities and risks of inserting AI …
[HTML][HTML] Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings: a systematic review
OT Jones, RN Matin, M Van der Schaar… - The Lancet Digital …, 2022 - thelancet.com
Skin cancers occur commonly worldwide. The prognosis and disease burden are highly
dependent on the cancer type and disease stage at diagnosis. We systematically reviewed …
dependent on the cancer type and disease stage at diagnosis. We systematically reviewed …
[HTML][HTML] Characteristics of publicly available skin cancer image datasets: a systematic review
D Wen, SM Khan, AJ Xu, H Ibrahim, L Smith… - The Lancet Digital …, 2022 - thelancet.com
Publicly available skin image datasets are increasingly used to develop machine learning
algorithms for skin cancer diagnosis. However, the total number of datasets and their …
algorithms for skin cancer diagnosis. However, the total number of datasets and their …
[HTML][HTML] Analysis of the ISIC image datasets: Usage, benchmarks and recommendations
Abstract The International Skin Imaging Collaboration (ISIC) datasets have become a
leading repository for researchers in machine learning for medical image analysis …
leading repository for researchers in machine learning for medical image analysis …
Artificial intelligence in digital pathology—new tools for diagnosis and precision oncology
In the past decade, advances in precision oncology have resulted in an increased demand
for predictive assays that enable the selection and stratification of patients for treatment. The …
for predictive assays that enable the selection and stratification of patients for treatment. The …