Omkar2118/datasets huggingface dataset:
Micro-Ultrasound Prostate Segmentation Dataset

This dataset comprises micro-ultrasound scans and human prostate annotations of 75 patients who underwent micro-ultrasound guided prostate biopsy at the University of Florida. All images and segmentations have been fully de-identified in the NIFTI format.

Under the "train" folder, you'll find three subfolders:

1. "micro_ultrasound_scans" contains micro-ultrasound images from 55 patients for training.
2. "expert_annotations" contains ground truth prostate segmentations annotated by our expert urologist.
3. "non_expert_annotations" contains prostate segmentations annotated by a graduate student.

In the "test" folder, there are five subfolders:

1. "micro_ultrasound_scans" contains micro-ultrasound images from 20 patients for testing.
2. "expert_annotations" contains ground truth prostate segmentations by the expert urologist.
3. "master_student_annotations" contains segmentations by a master's student.
4. "medical_student_annotations" contains segmentations by a medical student.
5. "clinician_annotations" contains segmentations by a urologist with limited experience in reading micro-ultrasound images.

If you use this dataset, please cite our paper: Jiang, Hongxu, et al. "MicroSegNet: A deep learning approach for prostate segmentation on micro-ultrasound images." Computerized Medical Imaging and Graphics (2024): 102326. DOI: https://doi.org/10.1016/j.compmedimag.2024.102326.

For any dataset-related queries, please reach out to Dr. Wei Shao:  weishao@ufl.edu.
MediaEval

Understanding what makes a video memorable has a very broad range of current applications, e.g., education and learning, content retrieval and search, content summarization, storytelling, targeted advertising, content recommendation and filtering. This task requires participants to automatically predict memorability scores for videos that reflect the probability for a video to be remembered over both a short and long term. Participants will be provided with an extensive data set of videos with memorability annotations, related information, pre-extracted state-of-the-art visual features, and Electroencephalography (EEG) recordings.

Subtask 1: How memorable is this video? - Video-based prediction: This task requires participants to automatically predict memorability scores for videos that reflect the probability for a video to be remembered. Participants are required to generate automatic systems that predict memorability scores of new videos based on the given video dataset and their memorability scores.

Subtask 2: How memorable is this video?- Generalization (optional): Participants will train their system on one of the two sources of data we provide and will test them on the other source of data. This is an optional subtask.

Subtask 3: Will this person remember this video? - EEG-based prediction: This task requires participants to automatically predict if a person will remember a video. Participants are required to generate automatic systems that predict if a person will remember a new video based on the given video dataset and their EEG record.

Introducing paper: Overview of The MediaEval 2022 Predicting Video Memorability Task
EAPD
Full name: Expert-labeled Aesthetics Perception Database

An expert benchmark aiming to comprehensively evaluate the aesthetic perception capacities of MLLMs.

Introducing paper: AesBench: An Expert Benchmark for Multimodal Large Language Models on Image Aesthetics Perception