An open access thyroid ultrasound image database

By L. Pedraza, C. Vargas, Fabián R. Narváez, O. Duran, E. Muñoz, E. Romero. Other Conferences, 2015

358 citations recorded by the source corpus.

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Thyroid Nodule Classification in Ultrasound Images by Fine-Tuning Deep Convolutional Neural Network (2017, methodology): “…work were from the following two databases: Database 1 is a publicly available thyroid ultrasound image database proposed by Pedraza et al. [16], consisting of 428 thyroid ultrasound images with the size 560 × 360, of which 357 cases are labelled as positive (with the TI-RADS scores 3, 4a,…”
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model (2022, methodology): “The experiments of glaucoma, thyroid cancer and melanoma diagnosis are conducted on REFUGE-2 dataset [14], BraTs-2021 dataset [15] and DDTI dataset [16], which contain 1200, 2000, 8046 samples, respectively.”
MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer (2023, methodology): “The other four public datasets REFUGE-2 (Fang et al. 2022), BraTs-2021 dataset (Baid et al. 2021), ISIC 2018 dataset(Milton 2019) and TNMIX dataset (Pedraza et al. 2015) are used to verify the model performance on multi-modal images, which are the optic-cup segmentation from fundus images, the…”
RadImageNet: An Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning (2022, background): “The thyroid dataset contains 349 US images with radiologistgenerated annotations collected from an open access thyroid image dataset (23).”