CoNIC Challenge

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About

Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained histology images enables the extraction of interpretable cell-based features that can be used in downstream explainable models in computational pathology (CPath). To help drive forward research and innovation for automatic nuclei recognition in CPath, we organise the Colon Nuclei Identification and Counting (CoNIC) Challenge. The challenge requires researchers to develop algorithms that perform segmentation, classification and counting of 6 different types of nuclei within the current largest known publicly available nuclei-level dataset in CPath, containing around half a million labelled nuclei.

The challenge consisted of two tasks:

Nuclear segmentation and classification
- Automatically localise the boundaries of different types of nuclei
Cellular composition
- Automatically predict the counts of different types of nuclei

Visit the challenge website, where you can find instructions on downloading the main dataset.

Method Manuscripts

Below we provide method description manuscripts that described each of the submissions to the challenge.

Team Name	Task One Position	Task Two Position
EPFL \| StarDist	1st	3rd
Pathology AI	3rd	1st
AI_medical	5th	2nd
MDC Berlin \| IFP Bern	2nd	9th
CIA Group	7th	4th
LSL000UD	4th	6th
Softsensor_Group	12th	5th
Arontier	6th	7th
MBZUAI_CoNiC	10th	8th
MAIIA	8th	18th
ciscNet	9th	11th
Denominator	11th	10th
BMS_LAB	13th	15th
GDPH_HC	14th	13th
TIA Warwick	24th	14th
conic-challenge-inescteam	15th	N/A
Jiffy Labs and CET CV Lab	23rd	16th
Aman	16th	N/A
DH-Goods	19th	17th
Bin	17th	20th
Sk	21st	19th
VNIT	20th	22nd
QuIL	N/A	23rd
IVG	26th	24th

Participant Docker Containers

To download Docker containers from the top challenge algorithms, click here.

WSI-Level Results

To download WSI-level results obtained after processing ~1.7K WSIs with the winning algorithms on each task of the challenge click here.