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Zifeng Wu, Chunhua Shen, Anton van den Hegnel attended the competition of Nuclei images segmentation task and won the first place!
Leaderboard (showing submitter’s ID):
http://miccai.cloudapp.net/competitions/83#results

 

Overview

Grading and diagnosis of tumors in cancer patients have traditionally been done by examination of tissue specimens under a powerful microscope by expert pathologists. While this process continues to be widely applied in clinical settings, it is not scalable to translational and clinical research studies involving hundreds or thousands of tissue specimens. State-of-the-art digitizing microscopy instruments are capable of capturing high-resolution images of whole slide tissue specimens rapidly. Computer aided segmentation and classification has the potential to improve the tumor diagnosis and grading process as well as to enable quantitative studies of the mechanisms underlying disease onset and progression.

The objective of this challenge is to evaluate and compare segmentation algorithms and to encourage the biomedical imaging community to design and implement more accurate and efficient algorithms. The challenge will evaluate the performance of algorithms for detection and segmentation of nuclei in a tissue image. Participants are asked to detect and segment all the nuclear material in a given set of image tiles extracted from whole slide tissue images.

This challenge uses image tiles from whole slide tissue images to reduce computational and memory requirements. The image tiles are rectangular regions extracted from a set of Glioblastoma and Lower Grade Glioma whole slide tissue images. Nuclei in each image tile in the training set have been manually segmented. Note that the tiles are not of the same size.

 


 

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Our ResNet38 models are included in Wolfram Neural Net Repo:

https://resources.wolframcloud.com/NeuralNetRepository/resources/Ademxapp-Model-A-Trained-on-ImageNet-Competition-Data

https://resources.wolframcloud.com/NeuralNetRepository/resources/Ademxapp-Model-A1-Trained-on-ADE20K-Data

https://resources.wolframcloud.com/NeuralNetRepository/resources/Ademxapp-Model-A1-Trained-on-Cityscapes-Data

https://resources.wolframcloud.com/NeuralNetRepository/resources/Ademxapp-Model-A1-Trained-on-PASCAL-VOC2012-and-MS-COCO-Data

 

 

 

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The AIML (Australian Institute for Machine Learning) team, who are Zifeng Wu, Chunhua Shen, Anton van den Hegnel, attended this medical imaging competition and won the 1st place for the task of segmentation.

Leaderboard:

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We were the 4th place in the JD Fashion Item Search Competition. https://fashion-challenge.github.io/rank.html https://fashion-challenge.github.io/#fashion-item

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Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation Proc. Int. Conf. Computer Vision 2017

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Slides can be accessed at https://cloudstor.aarnet.edu.au/plus/s/Oyqtc4DABsFvddb

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This work will be presented at IEEE Conf. Computer Vision and Pattern Recognition (CVPR) 2018.

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Detecting individual pedestrians in a crowd remains a challenging problem since the pedestrians often gather together and occlude each other in real-world scenarios. In this paper, we first explore how a state-of-the-art pedestrian detector is harmed by crowd occlusion via experimentation, providing insights into the crowd occlusion problem. Then, we propose a novel bounding box […]

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Accurately counting maize tassels is important for monitoring the growth status of maize plants. This tedious task, however, is still mainly done by manual efforts. In the context of modern plant phenotyping, automating this task is required to meet the need of large-scale analysis of genotype and phenotype. In recent years, computer vision technologies have […]

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This work was presented in IEEE Conf Computer Vision Pattern Recognition 2017 in Hawaii

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