python-pillow/Pillow 8.0.1

Kemenade, Hugo van; wiredfool,; Murray, Andrew; Clark, Alex M.; Karpinsky, Alexander; nulano,; Gohlke, Christoph; Dufresne, Jon; Crowell, Brian; Schmidt, David A.; Houghton, Alastair; Kopachev, Konstantin; Mani, Sandro; Landey, Steve; vashek,; Ware, Josh; Jason,; Caro, David; Kossouho, Steve; Lahd, Riley; Stanislau, T; Lee, Antony; Brown, Eric W.; Tonnhofer, Oliver; Bonfill, Mickael; Rowlands, Peter; Al-Saidi, Fahad; Górny, Michał; Korobov, Mikhail; Kurczewski, Marcin

doi:10.5281/zenodo.4118627

Cited by 2 publications

(1 citation statement)

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“…, Python 3.7.7, edfapi 3.1, EYELINK II CL v5.15, Eyelink GL Version 1.2 Sensor=AC7, EDF2ASC 3.1, librosa 0.8.040 , numpy 1.19.141 , pandas 1.1.142,43 , statsmodels 0.12.233 , shapely 1.7.144 , scikitimage 0.17.245 , pyrubberband 0.3.0, pydicom 2.1.246 , pydub 0.24.1, soundfile 0.10.3.post1, pyttsx3 2.90, pillow 8.0.147 , scikit-learn 0.23.248 , nltk 3.549 , syllables 1.0.0, moviepy 1.0.3 50 , opencv 3.4.2 51 , Ubuntu 18.04.5 LTS, espeak 1.48.04, ffmpeg 3.4.8, and rubberband-cli 1.8.1. Overview of the steps in the building of the dataset.…”

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confidence: 99%

REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays

Lanfredi,

Zhang,

Auffermann

et al. 2021

Preprint

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Deep learning has shown recent success in classifying anomalies in chest x-rays, but datasets are still small compared to natural image datasets. Supervision of abnormality localization has been shown to improve trained models, partially compensating for dataset sizes. However, explicitly labeling these anomalies requires an expert and is very time-consuming. We propose a method for collecting implicit localization data using an eye tracker to capture gaze locations and a microphone to capture a dictation of a report, imitating the setup of a reading room, and potentially scalable for large datasets. The resulting REFLACX (Reports and Eye-Tracking Data for Localization of Abnormalities in Chest X-rays) dataset was labeled by five radiologists and contains 3,032 synchronized sets of eye-tracking data and timestamped report transcriptions. We also provide bounding boxes around lungs and heart and validation labels consisting of ellipses localizing abnormalities and image-level labels. Furthermore, a small subset of the data contains readings from all radiologists, allowing for the calculation of inter-rater scores.

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mentioning

confidence: 99%

REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays

Lanfredi,

Zhang,

Auffermann

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays

et al. 2022

View full text Add to dashboard Cite

Deep learning has shown recent success in classifying anomalies in chest x-rays, but datasets are still small compared to natural image datasets. Supervision of abnormality localization has been shown to improve trained models, partially compensating for dataset sizes. However, explicitly labeling these anomalies requires an expert and is very time-consuming. We propose a potentially scalable method for collecting implicit localization data using an eye tracker to capture gaze locations and a microphone to capture a dictation of a report, imitating the setup of a reading room. The resulting REFLACX (Reports and Eye-Tracking Data for Localization of Abnormalities in Chest X-rays) dataset was labeled across five radiologists and contains 3,032 synchronized sets of eye-tracking data and timestamped report transcriptions for 2,616 chest x-rays from the MIMIC-CXR dataset. We also provide auxiliary annotations, including bounding boxes around lungs and heart and validation labels consisting of ellipses localizing abnormalities and image-level labels. Furthermore, a small subset of the data contains readings from all radiologists, allowing for the calculation of inter-rater scores.

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python-pillow/Pillow 8.0.1

Cited by 2 publications

References 0 publications

REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays

REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays

REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays

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