TissueNet: Detect Lesions in Cervical Biopsies Hosted By French Society of Pathology
The objective of this challenge is to detect the most severe epithelial lesions of the uterine cervix. The challenge brings together thousands of microscopic slides from different medical centers across France.
The French Society of Pathology and the Health Data Hub are very enthusiastic about the data challenge results! They show that AI will sooner or later be part of the tools that pathologists use on a daily basis.
— Dr. Philippe Bertheau, President of the French Society of Pathology
A biopsy is a sample of tissue examined at a microscopic level to diagnose cancer or signs of pre-cancer. Digital pathology has developed considerably over the past decade as it has become possible to work with digitized "whole slide images" (WSIs). These heavy image files contain all the information required to diagnose lesions as malignant or benign, yet present huge challenges to use effectively.
This challenge focused on epithelial lesions of the uterine cervix, and featured a unique collection of thousands expert-labeled WSIs collected from medical centers across France. This is a sizable dataset (700GB) of extremely high resolution images. Given the scale of the dataset, handling the data efficiently is a critical problem to solve in the process of developing an accurate approach to diagnosis.
In this competition, participants were tasked with building machine learning models that could predict the most severe lesions in each digital biopsy slide. What's more, participants needed to submit code for executing their solution on test data in the cloud, ensuring that the model could run fast enough on this large scale data to be useful in practice. This setup rewards models that perform well on unseen images and brings these innovations one step closer to impact.
The winning solutions used clever approaches to prioritize the parts of each slide to analyze further, and built computer vision pipelines to determine the most appropriate diagnosis for the selected tissue. Models were scored not just on their accuracy, but also on the impact of their errors (providing a large penalty for mistakes that have worse consequences in practice).
The top-performing model achieved over 76% accuracy in predicting the exact severity label of each slide across 4 ranked classes, including 95% accuracy for the most severe class of cancerous tissue. In addition, the top 3 solutions achieved >98% on-or-adjacent accuracy, meaning they reduced the more costly misclassifications that erred by more than one class to less than 2% of the 1,500+ slide test set!
All prize-winning solutions are available under an open source license for ongoing use and learning.
TissueNet: Detect Lesions in Cervical Biopsies: Rules and Terms of Data Use
DrivenData Competition Rules
One account per participant
You cannot sign up to DrivenData from multiple accounts and therefore you cannot submit from multiple accounts.
Private sharing of code
Privately sharing code or data outside of teams is not permitted.
Winner License Type: Open Source License
Winning solutions need to be made available under The MIT License (an open source software license commonly described at http://opensource.org/licenses/MIT) in order to be eligible for recognition and prize money if offered.
Documentation for winning solutions
Winning solutions must be documented using the Winning Model Documentation Template provided to top-ranking participants in order to be eligible for recognition and prize money if offered.
The number of submissions per day is restricted to a fixed value on a per-competition basis. Any attempt to circumvent this limit will result in disqualification.
External data is permitted as long as it is freely and publicly available to all Participants under a permissive open source license. Participants agree to ensure any external data or pre-trained models they use are publicly shared in the competition discussion forum.
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Data use and code sharing
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Disclaimer of Warranties
You understand that we cannot and do not guarantee or warrant that the Data or other files available for downloading from the internet or the Competition Website will be free of viruses or other destructive code. You are responsible for implementing sufficient procedures and checkpoints to satisfy your particular requirements for anti-virus protection and accuracy of data input and output, and for maintaining a means external to our site for any reconstruction of any lost data. RELEASED PARTIES WILL NOT BE LIABLE FOR ANY LOSS OR DAMAGE CAUSED BY A DISTRIBUTED DENIAL-OF-SERVICE ATTACK, VIRUSES OR OTHER TECHNOLOGICALLY HARMFUL MATERIAL THAT MAY INFECT YOUR COMPUTER EQUIPMENT, COMPUTER PROGRAMS, DATA OR OTHER PROPRIETARY MATERIAL DUE TO YOUR USE OF THE WEBSITE OR ANY SERVICES OR ITEMS OBTAINED THROUGH THE WEBSITE OR TO YOUR DOWNLOADING OF ANY MATERIAL POSTED ON IT, OR ON ANY WEBSITE LINKED TO IT.
YOUR USE OF THE COMPETITION WEBSITE, ITS CONTENT AND ANY SERVICES OR ITEMS OBTAINED THROUGH THE WEBSITE AND RECEIPT OF ANY PRIZES IS AT YOUR OWN RISK. THE WEBSITE, ITS CONTENT AND ANY SERVICES OR ITEMS OBTAINED THROUGH THE WEBSITE ARE PROVIDED ON AN “AS IS” AND “AS AVAILABLE” BASIS, WITHOUT ANY WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, BEYOND ANY NON-EXCLUDABLE GUARANTEES (DEFINED BELOW). NEITHER THE RELEASED PARTIES NOR ANY PERSON ASSOCIATED WITH RELEASED PARTIES MAKES ANY WARRANTY OR REPRESENTATION WITH RESPECT TO THE COMPLETENESS, SECURITY, RELIABILITY, QUALITY, ACCURACY OR AVAILABILITY OF THE COMPETITION WEBSITE. WITHOUT LIMITING THE FOREGOING, NEITHER RELEASED PARTIES NOR ANYONE ASSOCIATED WITH THE RELEASED PARTIES REPRESENTS OR WARRANTS THAT THE COMPETITION WEBSITE, ITS CONTENT OR ANY SERVICES OR ITEMS OBTAINED THROUGH THE WEBSITE WILL BE ACCURATE, RELIABLE, ERROR-FREE OR UNINTERRUPTED, THAT DEFECTS WILL BE CORRECTED, THAT OUR SITE OR THE SERVER THAT MAKES IT AVAILABLE ARE FREE OF VIRUSES OR OTHER HARMFUL COMPONENTS OR THAT THE COMPETITION WEBSITE OR ANY SERVICES OR ITEMS OBTAINED THROUGH THE WEBSITE WILL OTHERWISE MEET YOUR NEEDS OR EXPECTATIONS.
NOTHING IN THESE COMPETITION RULES LIMITS, EXCLUDES, OR MODIFIES OR PURPORTS TO LIMIT, EXCLUDE, OR MODIFY ANY STATUTORY CONSUMER GUARANTEE OR ANY IMPLIED CONDITION OR WARRANTY, THE EXCLUSION OF WHICH FROM THESE COMPETITION RULES WOULD CONTRAVENE ANY STATUTE OR CAUSE ANY PART OF THESE TERMS TO BE VOID (“NON-EXCLUDABLE GUARANTEES”). SUBJECT TO THE LIMITATIONS IN THE PRECEDING SENTENCE AND TO THE MAXIMUM EXTENT PERMITTED BY ANY MANDATORY PROVISIONS OF APPLICABLE LAW, THE RELEASED PARTIES HEREBY DISCLAIM ALL WARRANTIES OF ANY KIND, WHETHER EXPRESS OR IMPLIED, STATUTORY OR OTHERWISE, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF MERCHANTABILITY, NON-INFRINGEMENT AND FITNESS FOR PARTICULAR PURPOSE.
THE FOREGOING DOES NOT AFFECT ANY WARRANTIES WHICH CANNOT BE EXCLUDED OR LIMITED UNDER APPLICABLE LAW.
Governing Law and Jurisdiction
DrivenData shall be the sole interpreter of these Competition Rules.
All matters relating to the Competition Rules or the Competition and any dispute or claim arising therefrom or related thereto (in each case, including non-contractual disputes or claims), shall be governed by and construed in accordance with the internal laws of the Commonwealth of Massachusetts without giving effect to any choice or conflict of law provision or rule (whether of the Commonwealth of Massachusetts or any other jurisdiction).
The invalidity or unenforceability of any provision of these Competition Rules shall not affect the validity or enforceability of any other provision. In the event that any provision is determined to be invalid or otherwise unenforceable or illegal, these Competition Rules shall otherwise remain in effect and be construed in accordance with their terms as if the invalid or illegal provision was not contained herein.