Several datasets are fostering innovation in higher-level functions for everyone, everywhere. By providing this repository, we hope to encourage the research community to focus on hard problems. In this repository, we present our medical imaging DICOM files of patients from our User Tests and Analysis 4 (UTA4) study. Here, we provide a dataset of the used medical images during the UTA4 tasks. This repository and respective dataset should be paired with the dataset-uta4-rates repository dataset. Work and results are published on a top Human-Computer Interaction (HCI) conference named AVI 2020 (page). Results were analyzed and interpreted on our Statistical Analysis charts. The user tests were made in clinical institutions, where clinicians diagnose several patients for a Single-Modality vs Multi-Modality comparison. For example, in these tests, we used both prototype-single-modality and prototype-multi-modality repositories for the comparison. On the same hand, the hereby dataset repres
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The dataset contains full-spectral autofluorescence lifetime microscopic images (FS-FLIM) acquired on unstained ex-vivo human lung tissue, where 100 4D hypercubes of 256x256 (spatial resolution) x 32 (time bins) x 512 (spectral channels from 500nm to 780nm). This dataset associates with our paper "Deep Learning-Assisted Co-registration of Full-Spectral Autofluorescence Lifetime Microscopic Images with H&E-Stained Histology Images" (https://arxiv.org/abs/2202.07755) and "Full spectrum fluorescence lifetime imaging with 0.5 nm spectral and 50 ps temporal resolution" (https://doi.org/10.1038/s41467-021-26837-0). The FS-FLIM images provide transformative insights into human lung cancer with extra-dimensional information. This will enable visual and precise detection of early lung cancer. With the methodology in our co-registration paper, FS-FLIM images can be registered with H&E-stained histology images, allowing characterisation of tumour and surrounding cells at a celluar level with abs
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