data-science-bowl-2018.zipMt**xx 上传358.35 MB 文件格式 zip data-science-bow File descriptions • /stage1_train/* - training set images (images and annotated masks) • /stage1_test/* - stage 1 test set images (images only, you are predicting the masks) • /stage2_test/* (...
想想如果治愈更快的话,将会改变多少生命。 通过自动进行核检测,您可以帮助更快地解锁治疗方法-从罕见疾病到普通感冒。 Kaggle的深度学习教程使用Keras,在发散图像中查找原子核以推进医学发现竞赛 本教程说明如何使用构建深层神经网络,以在发散图像 点赞(0)踩踩(0)反馈 所需:9积分电信网络下载...
Imagine speeding up research for almost every disease, from lung cancer and heart disease to rare disorders. The 2018 Data Science Bowl offers our most ambitious mission yet: create an algorithm to automate nucleus detection. We’ve all seen people suffer from diseases like cancer, heart disease...
As with any human-annotated dataset, you may find various forms of errors in the data. You may manually correct errors you find in the training set. The dataset will not be updated/re-released unless it is determined that there are a large number of systematic errors. The masks of the ...
【(Kaggle)2018 Data Science Bowl夺冠方案分享】《topcoders, 1st place solution | 2018 Data Science Bowl | Kaggle》 http://t.cn/RmuTtYi
The Revised Train set(https://github.com/lopuhin/kaggle‐dsbowl‐2018‐dataset‐fixes) 2009 ISBI (http://murphylab.web.cmu.edu/data/2009_ISBI_Nuclei.html) Weebly (https://nucleisegmentationbenchmark.weebly.com/) TNBC (https://zenodo.org/record/1175282#.Ws2n_vkdhfA) ...
『 kaggle』kaggle-DATA-SCIENCE-BOWL-2018(U-net方法) 1. 赛题背景 通过自动化细胞核检测,有利于检测细胞对各种治疗方法的反应,了解潜在生物学过程。队伍需要分析数据观察模式,抽象出问题并通过建立计算机模型识别各种条件下的一系列细胞核。 2. 数据预处理...
全卷积神经网路【U-net项目实战】U-Net网络练习题: Kaggle - 2018 Data Science Bowl,因为Kaggle有该比赛,而且code写的很简单易懂,于是乎拿来玩一下。https://www.kaggle.com/keegil/keras-u-net-starter-lb-0-277?scriptVersionId=21855/notebook与U-Net相关的开源项目
(missing: https://www.kaggle.com/static/assets/6084.f4312d5ceab69cdedec9.js) at r.f.j (https://www.kaggle.com/static/assets/runtime.js?v=dc5e9e2d37e9ce537d83:1:10505) at https://www.kaggle.com/static/assets/runtime.js?v=dc5e9e2d37e9ce537d83:1:1295 at Array.reduce (<...
At the end of competition we tried to train a model predicting only data used for post processing without vector fields and the result was significantly worse. Significantly reducing the loss of vectors to the center of nuclei had a little impact to the results, so most likely vectors from co...