Загрузка страницы

HuBMAP Awards: Hacking the Kidney on Kaggle

The HuBMAP Awards: Hacking the Kidney on Kaggle event is a celebration of the innovation and achievements of the Kaggle participants who competed in the HuBMAP: Hacking the Kidney competition.

HuBMAP is an initiative to develop an open and global platform to map healthy cells in the human body. Understanding the healthy state lays the foundation for understanding disease. In humans, the proper functioning of organs and tissues is dependent on the interaction, spatial organization, and specialization of all our cells. In the same way that cities are defined by their communities, neighborhoods and homes. Through HuBMAP a vast amount of data has been generated which needs to be presented for interpretation quickly, efficiently and accurately. Breakthroughs in artificial intelligence and deep learning have opened new avenues to digest and make sense of big data. We wanted to decode the kidney to identify functional communities composed of single cells in three dimensions. To achieve this, we initiated a Kaggle competition to crowdsource this effort of hacking the kidney and bring together the collective intelligence of biologists and machine learning scientists globally. This competition garnered submission from over 1200 teams around the world and achieved state-of-the-art results. Submissions were judged by eminent leaders and pioneers in the fields of kidney biology and machine learning. A big thanks to our innovation and cyber sponsors Google, Roche, Deerfield Healthcare, Deloitte, Pistoia Alliance, CAS and Maven Wave.

Видео HuBMAP Awards: Hacking the Kidney on Kaggle канала Kaggle
Показать
Комментарии отсутствуют
Введите заголовок:

Введите адрес ссылки:

Введите адрес видео с YouTube:

Зарегистрируйтесь или войдите с
Информация о видео
21 мая 2021 г. 23:51:35
01:46:51
Другие видео канала
6th Place Solution for the Google Smartphone Decimeter Challenge | Kaggle6th Place Solution for the Google Smartphone Decimeter Challenge | KaggleInterview with Competitions Grandmaster Mario Filho (in Brazilian Portuguese)| KaggleInterview with Competitions Grandmaster Mario Filho (in Brazilian Portuguese)| KaggleGetting Curious: What it takes to build a TPU | KaggleGetting Curious: What it takes to build a TPU | KaggleKaggle Live Coding: Is it getting easier or harder to become a kernels expert? | KaggleKaggle Live Coding: Is it getting easier or harder to become a kernels expert? | KaggleAutomatic Speech Recognition | by Darragh Hanley | Kaggle Days Dubai | KaggleAutomatic Speech Recognition | by Darragh Hanley | Kaggle Days Dubai | KaggleKaggle Reading Group: XLNet (Part 2) | KaggleKaggle Reading Group: XLNet (Part 2) | KaggleDeep Learning: When and How | by Mikhail Trofimov | Kaggle Days WarsawDeep Learning: When and How | by Mikhail Trofimov | Kaggle Days WarsawHow to Start a DS Career with Kaggle | by Evgeny Patekha | Kaggle Days WarsawHow to Start a DS Career with Kaggle | by Evgeny Patekha | Kaggle Days WarsawKaggle Live-coding: Emoji Analysis (part 2) | KaggleKaggle Live-coding: Emoji Analysis (part 2) | KaggleKaggle Reading Group: XLNet (Part 3) | KaggleKaggle Reading Group: XLNet (Part 3) | KaggleKaggle Live-Coding: Reproducing Research Project (part 3) | KaggleKaggle Live-Coding: Reproducing Research Project (part 3) | KaggleShould you keep the tweet?: Balancing reproducibility, open data and participant privacy | KaggleShould you keep the tweet?: Balancing reproducibility, open data and participant privacy | KaggleHow to succeed in code (kernel) competitions | Dmitry Gordeev | Kaggle DaysHow to succeed in code (kernel) competitions | Dmitry Gordeev | Kaggle DaysKaggleX Final Project Presentation with Zilin Lu | KaggleKaggleX Final Project Presentation with Zilin Lu | KaggleKaggle Reading Group: Language Models are Unsupervised Multitask Learners (GPT-2) | KaggleKaggle Reading Group: Language Models are Unsupervised Multitask Learners (GPT-2) | KaggleKaggle Reading Group: Multi-Task DNNs for Natural Language Understanding (Part 2) | KaggleKaggle Reading Group: Multi-Task DNNs for Natural Language Understanding (Part 2) | KaggleKaggle Reading Group: Probing the Need for Visual Context in Multimodal Machine Translation| KaggleKaggle Reading Group: Probing the Need for Visual Context in Multimodal Machine Translation| KaggleKaggle Reading Group: Probing Neural Network Comprehension of Natural Language Arguments | KaggleKaggle Reading Group: Probing Neural Network Comprehension of Natural Language Arguments | KaggleKaggle Reading Group: On NMT Search Errors and Model Errors: Cat Got Your Tongue?  | KaggleKaggle Reading Group: On NMT Search Errors and Model Errors: Cat Got Your Tongue? | KaggleKaggle Reading Group: Probing Neural Network Comprehension of Natural Language Arguments (Part 2)Kaggle Reading Group: Probing Neural Network Comprehension of Natural Language Arguments (Part 2)Lessons Learned from the Kaggle Community | by Anthony Goldbloom CEO | Kaggle Days WarsawLessons Learned from the Kaggle Community | by Anthony Goldbloom CEO | Kaggle Days Warsaw
Яндекс.Метрика