Konica-Minolta Pathological Image Segmentation Challenge
1st place - $10,000
2nd place - $7,000
3rd place - $5,000
4th place - $3,000
5th place - $1,000
This contest aims for segmentation for pathological images, which will help the diagnosis of cancers.
Requirements to Win a Prize
In order to receive a prize, you must do all the following:
In the medical field, compared to Radiology, digitization of Pathology was rather delayed. By the spread of WSI (Whole Slide Imaging) capable of digitally shooting the entire specimen, the situation has changed and digitization is rapidly proceeding. With the increase of large amounts of digital data, the burden of interpretation by the pathologist has increased intensively, and it is coming to the limits of human diagnosis.
Techniques for processing them by machine learning such as Deep Learning and applying them to individual cell recognition and cancer diagnosis have also been developed along with various image recognition contests.
With these backgrounds, Konica Minolta intends to develop a recognition technology that distinguishes between the cancer region and other regions that are positioned as the basis for all digital pathological image analysis.
The challenge is not easy, as currently an expert pathologist needs to comprehensively judge while looking at the individual and the whole area of the images. We believe with the latest Semantic segmentation technology, we should be able to get a model that can demonstrate the same performance as human beings.
We will be looking forward for many people to participate to this challenge!
All images are cropped in the shape of 500 * 500. 168 annotated images are provided as the training data for your development and validation. You are asked to build a model which can take an 500 * 500 image as input and output its corresponding mask.
Data Description and Scoring
There are 168 annotated images for training and 162 images for testing. The data can be downloaded through the Google drive link. For each image named $NAME (e.g., i105404), we will provide the following data:
Your example submissions will be evaluated against the training data. Your full submissions will be evaluated against the testing data. The provisional test scores are based on a fixed subset of 81 images in testing data, while the system test scores are based on the other 81 images in the testing data.
We are using a combination of two metrics: micro-F1 and Dice Index (DI). The final score is computed as the following formula.
Final Score = 1000000.0 * (micro-F1 + DI) / 2.0
Considering all images together, the micro-F1 score will be defined based on pixels. For each pixel in any image, there are 4 cases:
Let the total number of True Positive be TP, the total number of False Positive as FP, the total number of False Negative as FN, and the total number of True Negative as TN.
The precision (P) is defined as TP / (TP + FP) and the recall (R) is defined as TP / (TP + FN). The micro-F1 score is computed as 2 * P * R / (P + R). Specially, when TP + FP equals 0, we define P as 1; when TP + FN equals 0, we define R as 1; when P + R equals 0, we define F1 as 0.
For each image, given a set of pixels G annotated as white (1) in the ground truth and a set of pixels S predicted as white (1) in your submission, if the intersection between G and S is X, Dice Index is defined as 2 * |X| / (|G| + |S|). Specially, when |G| + |S| equals 0, we define the Dice Index as 1.
For a set of images, we will average the Dice Index of each image and use this as DI.
During the contest, only your results will be submitted. You will submit code which implements only one function, getURL(). Your function will return a String corresponding to the URL of your answer (.zip).
This .zip file should include results (i.e., $NAME_mask.txt) of all training and testing data (330 in total). Each result is a .txt file of a 500 * 500 binary matrix as we described before. If some result files are missing, your submission will receive a score of -1.
You may use different names for the .zip file but should keep the same structure as follows.
submission.zip |-- i105404_mask.txt |-- i117557_mask.txt |-- ...
You may upload your .zip file to a cloud hosting service such as Dropbox which can provide a direct link to your .zip file. To create a direct sharing link in Dropbox, right click on the uploaded file and select share. You should be able to copy a link to this specific file which ends with the tag "?dl=0". This URL will point directly to your file if you change this tag to "?dl=1". You can then use this link in your getURL() function. Another common example is to use Google drive for sharing the link. If you choose that, please use the following format to create a direct sharing link: "https://drive.google.com/uc?export=download&id=" + id; You can use any other way to share your result file but make sure the link you provide should open the filestream directly.
Your complete code that generates these results will be tested at the end of the contest.
References and CitationsFollowing are good reference and starting point to learn more about the challenge and solutions.
 Janowczyk, Andrew, and Anant Madabhushi. "Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases." Journal of pathology informatics 7 (2016).
The pathology images and the portion of annotation data used in this challenge is from [1,3]. We appreciate greatly to Dr. Janowczyk for allowing us to use and manipulate the dataset in this challenge.
Terms and NDA
This challenge will follow the below standard Topcoder Terms and NDA
 Standard Terms for TopCoder Competitions v2.1 - https://www.topcoder.com/challenge-details/terms/detail/21193/
 Appirio NDA 2.0 - https://www.topcoder.com/challenge-details/terms/detail/21153/
This problem statement is the exclusive and proprietary property of TopCoder, Inc. Any unauthorized use or reproduction of this information without the prior written consent of TopCoder, Inc. is strictly prohibited. (c)2020, TopCoder, Inc. All rights reserved.