Omkar2118/datasets huggingface dataset:
AMPS
Full name: Auxiliary Mathematics Problems and Solutions

AMPS contains over 100,000 problems pulled from Khan Academy and approximately 5 million problems generated from manually designed Mathematica scripts.

Introducing paper: Measuring Mathematical Problem Solving With the MATH Dataset
stwstrategyhn1

This collection contains 156 cases of MAASTRO Head and Neck images and RTStruct contours. Where PET exists, the frame of reference UID of the CT has been transferred to the PET. This collection may not be used for commercial purposes. This collection is freely available to browse, download, and use for scientific and educational purposes as outlined in the Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0) https://creativecommons.org/licenses/by-nc/3.0/. Please be sure to include the following citation if you make use of this data set : "Aerts, H. J. W. L., Velazquez, E. R., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Carvalho, S., … Lambin, P. (2014, June 3). Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications. Nature Publishing Group. http://doi.org/10.1038/ncomms5006."
glioma

Purpose: Patients with 1p/19q codeleted low-grade glioma (LGG) have longer overall survival and better treatment response than patients with 1p/19q intact tumors. Therefore, it is relevant to know the 1p/19q status. To investigate whether the 1p/19q status can be assessed prior to tumor resection, we developed a machine learning algorithm to predict the 1p/19q status of presumed LGG based on preoperative MRI.

Experimental design: Preoperative brain MR images from 284 patients who had undergone biopsy or resection of presumed LGG were used to train a support vector machine algorithm. The algorithm was trained on the basis of features extracted from post-contrast T1-weighted and T2-weighted MR images and on patients' age and sex. The performance of the algorithm compared with tissue diagnosis was assessed on an external validation dataset of MR images from 129 patients with LGG from The Cancer Imaging Archive (TCIA). Four clinical experts also predicted the 1p/19q status of the TCIA MR images.

Results: The algorithm achieved an AUC of 0.72 in the external validation dataset. The algorithm had a higher predictive performance than the average of the neurosurgeons (AUC 0.52) but lower than that of the neuroradiologists (AUC of 0.81). There was a wide variability between clinical experts (AUC 0.45-0.83).

Conclusions: Our results suggest that our algorithm can noninvasively predict the 1p/19q status of presumed LGG with a performance that on average outperformed the oncological neurosurgeons. Evaluation on an independent dataset indicates that our algorithm is robust and generalizable.
All Conference Alert
Full name: All Conference Alert

All Conference Alert is a tech startup dedicated to helping organizers and attendees of academic conferences, seminars, workshops, and webinars. Our platform allows organizers to list and promote their events, covering over 150 academic subjects across 135 countries.

We provide free conference alerts to more than 1 million subscribers, ensuring researchers, scholars, and professionals stay informed about relevant events in their fields. Whether you're looking for business, engineering, medical, or technology conferences in the USA in 2025, All Conference Alert is your go-to source for discovering upcoming international conferences.

Introducing paper: 100,000 Podcasts: A Spoken English Document Corpus