1 code implementation • 22 May 2023 • Ashish Sinha, Jeremy Kawahara, Arezou Pakzad, Kumar Abhishek, Matthieu Ruthven, Enjie Ghorbel, Anis Kacem, Djamila Aouada, Ghassan Hamarneh
In recent years, deep learning (DL) has shown great potential in the field of dermatological image analysis.
no code implementations • 12 Jan 2023 • Mohamed Akrout, Bálint Gyepesi, Péter Holló, Adrienn Poór, Blága Kincső, Stephen Solis, Katrina Cirone, Jeremy Kawahara, Dekker Slade, Latif Abid, Máté Kovács, István Fazekas
Similar to recent applications of generative models, our study suggests that diffusion models are indeed effective in generating high-quality skin images that do not sacrifice the classifier performance, and can improve the augmentation of training datasets after curation.
1 code implementation • 2 May 2021 • Mengliu Zhao, Jeremy Kawahara, Kumar Abhishek, Sajjad Shamanian, Ghassan Hamarneh
Our lesion tracking algorithm achieves an average matching accuracy of 88% on a set of detected corresponding pairs of prominent lesions of subjects imaged in different poses, and an average longitudinal accuracy of 71% when encompassing additional errors due to lesion detection.
no code implementations • 4 Jun 2019 • Jeremy Kawahara, Ghassan Hamarneh
Skin conditions are a global health concern, ranking the fourth highest cause of nonfatal disease burden when measured as years lost due to disability.
no code implementations • 14 Mar 2017 • Jeremy Kawahara, Ghassan Hamarneh
We reformulate the task of classifying clinical dermoscopic features within superpixels as a segmentation problem, and propose a fully convolutional neural network to detect clinical dermoscopic features from dermoscopy skin lesion images.