Showing posts with label SIIM. Show all posts
Showing posts with label SIIM. Show all posts

Monday, September 14, 2020

[SIIM CMIMI20] A DICOM Framework for Machine Learning Pipelines against Real-Time Radiology Images

Today I presented our Niffler open-source framework at SIIM CMIMI 2020 at ML Algorithms & Toolkits + Infrastructure to Support ML session. We had slightly more than 100 participants, including the 90+ attendees and 10 panelists. We had 8 minutes presentations followed by a unified Q&A session. My presentation slides are given below.

 
This is my virtual conference experience where I presented a paper. I have attended several virtual conferences such as KDD, EuroSys, and DisCoTec this year, thanks to COVID19 lock-down. I also presented my work at IEEE SDS this year. However, SDS was a recording. We already had pre-recorded the videos and shared. As such, CMIMI becomes my first virtual conference experience where I was also a presenter.
 
I miss attending conferences in-person. The virtual conferences do not feel the same at all. Yes, we still see the presentations. But I value that coffee-hour talks, going out with newly made friends/colleagues, finding potential collaborators, personal connections, and many more. But something is better than nothing. We could still watch and listen to the presentations and interact with the presenters. But the face-to-face communication -- I miss that. 
 
I hope we will overcome the pandemic together and be back to in-person or even better - hybrid conferences soon! KDD 2021 has planned to be hybrid, online as well as on-site in Singapore. I am optimistic! The 2 days with CMIMI were great. I wish success to everyone in their research.

Wednesday, June 29, 2016

SPREAD — System for Sharing and Publishing Research Data

We presented our work on MEDIator/SPREAD at SIIM 2016 in Portland, OR. 
 
In this study we developed a set of web-services that abstract the task of replicating and publishing subsets of data that are stored in curated biomedical data repositories. The overarching goal is to develop tools that can be configured against data repositories and give researchers, during the course of a research study, the ability to seamlessly share, with collaborators, data subsets; and upon conclusion of the study, publish the datasets for wider consumption.
 
The full paper can be found here.