3.5k citations · 4.9k across the 10 of their papers we have counts for
12 papers
A Vision-Language Foundation Model for Zero-shot Clinical Collaboration and Automated Concept Discovery in Dermatology
Siyuan Yan, Xieji Li, Dan Mo +28
Medical foundation models have shown promise in controlled benchmarks, yet widespread deployment remains hindered by reliance on task-specific fine-tuning. Here, we introduce DermF…
Derm1M: A Million-scale Vision-Language Dataset Aligned with Clinical Ontology Knowledge for Dermatology
Siyuan Yan, Ming Hu, Yiwen Jiang +5
The emergence of vision-language models has transformed medical AI, enabling unprecedented advances in diagnostic capability and clinical applications. However, progress in dermato…
A Multimodal Vision Foundation Model for Clinical Dermatology
Siyuan Yan, Zhen Yu, Clare Primiero +22
Diagnosing and treating skin diseases require advanced visual skills across domains and the ability to synthesize information from multiple imaging modalities. While current deep l…
Dermatologist-like explainable AI enhances melanoma diagnosis accuracy: eye-tracking study
Tirtha Chanda, Sarah Haggenmueller, Tabea-Clara Bucher +19
Artificial intelligence (AI) systems have substantially improved dermatologists' diagnostic accuracy for melanoma, with explainable AI (XAI) systems further enhancing clinicians' c…
Automated dermatoscopic pattern discovery by clustering neural network output for human-computer interaction
Lidia Talavera-Martinez, Philipp Tschandl
Background: As available medical image datasets increase in size, it becomes infeasible for clinicians to review content manually for knowledge extraction. The objective of this st…
Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma
Tirtha Chanda, Katja Hauser, Sarah Hobelsberger +32
Although artificial intelligence (AI) systems have been shown to improve the accuracy of initial melanoma diagnosis, the lack of transparency in how these systems identify melanoma…
The Effects of Skin Lesion Segmentation on the Performance of Dermatoscopic Image Classification
Amirreza Mahbod, Philipp Tschandl, Georg Langs +2
Malignant melanoma (MM) is one of the deadliest types of skin cancer. Analysing dermatoscopic images plays an important role in the early detection of MM and other pigmented skin l…
A Patient-Centric Dataset of Images and Metadata for Identifying Melanomas Using Clinical Context
Veronica Rotemberg, Nicholas Kurtansky, Brigid Betz-Stablein +21
Prior skin image datasets have not addressed patient-level information obtained from multiple skin lesions from the same patient. Though artificial intelligence classification algo…
Detecting cutaneous basal cell carcinomas in ultra-high resolution and weakly labelled histopathological images
Susanne Kimeswenger, Elisabeth Rumetshofer, Markus Hofmarcher +5
Diagnosing basal cell carcinomas (BCC), one of the most common cutaneous malignancies in humans, is a task regularly performed by pathologists and dermato-pathologists. Improving h…
Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)
Noel Codella, Veronica Rotemberg, Philipp Tschandl +9
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…
Diagnostic Accuracy of Content Based Dermatoscopic Image Retrieval with Deep Classification Features
Philipp Tschandl, Giuseppe Argenziano, Majid Razmara +1
Background: Automated classification of medical images through neural networks can reach high accuracy rates but lack interpretability. Objectives: To compare the diagnostic accura…
The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Philipp Tschandl, Cliff Rosendahl, Harald Kittler
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. We…