Alzheimer's Disease Neuroimaging Initiative (ADNI) is a multisite study that aims to improve clinical trials for the prevention and treatment of Alzheimer’s disease (AD).[1] This cooperative study combines expertise and funding from the private and public sector to study subjects with AD, as well as those who may develop AD and controls with no signs of cognitive impairment.[2] Researchers at 63 sites in the US and Canada track the progression of AD in the human brain with neuroimaging, biochemical, and genetic biological markers.[2][3] This knowledge helps to find better clinical trials for the prevention and treatment of AD. ADNI has made a global impact,[4] firstly by developing a set of standardized protocols to allow the comparison of results from multiple centers,[4] and secondly by its data-sharing policy which makes available all at the data without embargo to qualified researchers worldwide.[5] To date, over 1000 scientific publications have used ADNI data.[6] A number of other initiatives related to AD and other diseases have been designed and implemented using ADNI as a model.[4] ADNI has been running since 2004 and is currently funded until 2021.[7] Source: Wikipedia, https://en.wikipedia.org/wiki/Alzheimer%27s_Disease_Neuroimaging_Initiative
Enlarge the dataset to understand how image background effect the Computer Vision ML model. With the following topics: Blur Background / Segmented Background / AI generated Background/ Bias of tools during annotation/ Color in Background / Dependent Factor in Background/ LatenSpace Distance of Foreground/ Random Background with Real Environment! We introduce XIMAGENET-12, an explainable benchmark dataset with over 200K images and 15,600 manual semantic annotations. Covering 12 categories from ImageNet to represent objects commonly encountered in practical life and simulating six diverse scenarios, including overexposure, blurring, color changing, etc., Our research builds upon the foundation laid by "Noise or Signal: The Role of Image Backgrounds in Object Recognition" (Xiao et al., ICLR 2022), "Explainable AI: Object Recognition With Help From Background" (Qiang et al., ICLR Workshop 2022), reinforced the notion that models trained solely on backgrounds can substantially improve accuracy. One noteworthy discovery highlighted in their studies is that more accurate models tend to rely less on backgrounds.
The Industrial Objects in Varied Contexts (InVar) Dataset was internally produced by our team and contains 100 objects in 20800 total images (208 images per class). The objects consist of common automotive, machine and robotics lab parts. Each class contains 4 sub-categories (52 images each) with different attributes and visual complexities. White background (D_wh): The object is against a clean white background and the object is clear, centred and in focus. Stationary Setup (D_st): These images are also taken against a clean background using a stationary camera setup, with uncentered objects at a constant distance. The images have lower DPI resolution with occasional cropping. Handheld (D_ha): These images are taken with the user holding the objects, with occasional occluding. Cluttered background (D_cl): These images are taken with the object placed along with other objects from the lab in the background and no occlusion.
To tackle the challenge of obtaining out-of-distribution (OOD) data for LVQA models, we introduced a novel dataset named 3U-VQA dataset (Usual, Unusual and Unknown object scenarios for LVQA with difficulty scoring dataset). The dataset comprises question and image sets. Each instance in the questions set is associated with a set of features representing the question-related criteria set. The questions and their ground truth answers are written using placeholders for the objects and their features, which can be specified based on the user needs and requirements. When creating the questions, we avoided deliberately binary (Yes/No) questions to prevent potential bias caused by the question's type in the model response.
B-XAIC consists of 50K small molecules represented as graphs and includes 7 graph classification tasks, each with ground truth labels and corresponding explanations.
Reasoning over spans of tokens from different parts of the input is essential for natural language understanding (NLU) tasks such as fact-checking (FC), machine reading comprehension (MRC) or natural language inference (NLI). We introduce SpanEx, a multi-annotator dataset of human-annotated span interaction explanations for two NLU tasks: NLI and FC. SpanEx - consists of 7071 instances annotated for span interactions. - the first dataset with human phrase-level interaction explanations with explicit labels for interaction types. - annotated by three annotators, which opens new avenues for studies of human explanation agreement -- an understudied area in the explainability literature.