Search Results for author:
Found 8 papers, 3 papers with code
The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available datasets of dermatoscopic images.
Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)
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 dermoscopic images of skin.
The Effects of Skin Lesion Segmentation on the Performance of Dermatoscopic Image Classification
In this study, we explicitly investigated the impact of using skin lesion segmentation masks on the performance of dermatoscopic image classification.
Diagnostic Accuracy of Content Based Dermatoscopic Image Retrieval with Deep Classification Features
Methods: A neural network was trained to predict disease classes on dermatoscopic images from three retrospectively collected image datasets containing 888, 2750 and 16691 images respectively.
Detecting cutaneous basal cell carcinomas in ultra-high resolution and weakly labelled histopathological images
The aim of this study is to evaluate whether it is possible to detect basal cell carcinomas in histological sections using attention-based deep learning models and to overcome the ultra-high resolution and the weak labels of whole slide images.
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.
Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma
To evaluate this system, we conducted a three-part reader study to assess its impact on clinicians' diagnostic accuracy, confidence, and trust in the XAI-support.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Automated dermatoscopic pattern discovery by clustering neural network output for human-computer interaction
Background: As available medical image datasets increase in size, it becomes infeasible for clinicians to review content manually for knowledge extraction.
