Omkar2118/datasets huggingface dataset:
MICCAI 2015 Head and Neck Challenge

This database is provided and maintained by Dr. Gregory C Sharp (Harvard Medical School – MGH, Boston) and his group.

The data here provided have been used for the “Head and Neck Auto Segmentation MICCAI Challenge (2015)”.
To cite the challenge or the data, please refer to:
Raudaschl, P. F., Zaffino, P., Sharp, G. C., Spadea, M. F., Chen, A., Dawant, B. M., … & Jung, F. (2017).
Evaluation of segmentation methods on head and neck CT: Auto‐segmentation challenge 2015.
Medical Physics, 44(5), 2020-2036.

PDDCA version 1.4.1 comprises 48 patient CT images from the Radiation Therapy Oncology Group (RTOG) 0522 study (a multi-institutional clinical trial led by Dr Kian Ang), together with manual segmentation of left and right parotid glands, brainstem, optic chiasm, optic nerves (both left and right), mandible, submandibular glands (both left and right) and manual identification of bony landmarks.
We give this data to the community in the hopes that it will be helpful. Any errors in delineation and markup are ours, and are not the fault of participating doctors.
Please see pddca.odt for complete information.
For practical reasons the database is split in 3 zipped files.
Part 3 contains images with some of the above listed structures missing.

Details regarding the Challenge Train/Test Splits can be found in the dataset description
NR2R
Full name: Night RAW to RGB

To form the collection of nighttime RAW samples, we first selected a total of 150 images with the spatial resolution at 3464×5202 from the training and validation sets provided by the night image challenge. And then these RAW images are pre-processed to best produce noise-free samples using a notable CNN based denoiser. This is because nighttime imaging experiences a very challenging situation with heavy noises incurred by high ISO setting under poor illumination condition (e.g., underexposure).

We applied a two-stage process to derive the corresponding RGB image of each RAW input. We first used a simple ISP that was comprised of linear demosaicing, gray-world white balance, color correction, and gamma correction to convert each denoised RAW input to its RGB format for groundtruth illumination estimation. To this aim, we mark the “White Patch” from each converted RGB, where the patch is presented in neutral gray, and its RGB channels are approximately the same. Since the gray surface presumably reflects all incoming light radiation, it can be used to represent the ground truth illumination of the RAW image accordingly. We then perform the 2-stage labeling using the illumination from the 1-stage. Specifically, first we get the correct color image by a serial operations including linear demosaicing, white balance using the label white balance and color correction with the camera inner color correction matrix (CCM). The brightness adjustment consists of local and global tone mapping jointly. Since local tone mapping requires fine-grained adjustment of each small patch in the scene, it is difficult to annotate it manually. Therefore, we use a pre-trained local tone mapping model to fulfill the task. Since the pre-trained tone mapping network was trained using daytime image, it is good for local adjustment, but fails to control the global brightness. We save the model output using a 16-bit intermediate format in PNG, and then import it into the Lightroom app to adjust the global exposure, brightness, shadows and contrast manually for final high-quality RGB image rendering, with which we emulate the image rendering knowledge from Professional Photographers. Thereafter, we successfully obtain a high-resolution nighttime RAW-RGB image dataset.

Introducing paper: Rendering Nighttime Image Via Cascaded Color and Brightness Compensation
160_subset
Full name: 160x160 subset

the 160x160 subset of the GasHisSDB dataset.

Introducing paper: GasHisSDB: A New Gastric Histopathology Image Dataset for Computer Aided Diagnosis of Gastric Cancer
Basketball Ballistic raw sequences

Ballistic trajectories

Introducing paper: Ball 3D Localization From A Single Calibrated Image