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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.


KONICA MINOLTA

Pathological Image Segmentation

Challenge 


In computer vision, image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as super-pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.

What is image segmentation?

In the medical field, compared to Radiology, digitization of Pathology was rather delayed. As a result of 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.

Konica Minolta Pathological Image Segmentation

Challenge on Topcoder

CHALLENGE NAME

 Pathological Image Segmentation


DATE

July 14th, 2017


TIME

12:00 PM EDT ,July 14th,2017 to 12:00 PM EDT , Aug 4th,2017


OBJECTIVE:

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

CHALLENGE DETAILS

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