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2021 亚太眼科学会大数据竞赛(APTOS)。队伍:UCAS_DM_GTL,初赛rank: 85/10006。比赛官网:https://tianchi.aliyun.com/competition/entrance/531929/information

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2021-APTOS-Big-Data-Challenge

[2021 亚太眼科学会大数据竞赛] | [队伍:UCAS_DM_GTL] | [Leaderboard]

by Huixuan Chi, Linyan Huang and He Zhu

2021亚太眼科学会大数据竞赛是由亚太眼科学会 (Asia Pacific Tele-Ophthalmology Society,APTOS)主办的全球人工智能挑战赛,其本主题是预测糖尿病性黄斑水肿 (DME) 患者的Anti-VEGF抗血管内皮生长因子(简称Anti-VEGF)治疗转归。同时,也是UCAS 2021秋季数据挖掘课程的大作业之一,这是我们的技术报告

由于我和zh同学都不是研究CV方向的(而且这个比赛是更加专业的医学图像分割问题),所以后期基本就是放弃了。

初赛结果:

  • rank: 85 / 10006
  • score: 0.4104

Date Score
2021-10-08 0.4104
2021-10-07 0.3973
2021-10-02 0.3807
2021-10-02 0.3716
2021-10-01 0.2853

Download dataset

v2版本数据集:https://drive.google.com/u/0/uc?id=1Wc0CmqeZg_gJkiiqoB1EZT0S0seB1MF4&export=download

数据集补充特征(pkl): https://drive.google.com/u/0/uc?id=1h2aHyAxEaVbM23YGP6dr_1pdwWIbG2NT&export=download

数据集补充特征(pkl-152):https://drive.google.com/u/0/uc?id=1C5_dRA1BmPOSjhlyrLxGO9LOhd5dXxao&export=download

原始仅切割的数据集split:https://drive.google.com/u/0/uc?id=1i9tnBbfM3tkO5GyMtytsZcUIxU1Gyt0W&export=download

v1版本数据集(已弃用):https://drive.google.com/file/d/1bx3x2dchtwgp-pBAqp0LzacLxY0sB0Gr/view?usp=sharing

使用:下载并解压之后,放到./dataset(新建一个目录,和README同一级)。详细情况可以参见文末的目录树。

ResNet预训练模型下载

cd data/
# download ResNet-50
wget https://download.pytorch.org/models/resnet50-19c8e357.pth
# download ResNet-152 
wget https://download.pytorch.org/models/resnet152-b121ed2d.pth

Branch Usage

baseline: tianchi_v4

《git的一些使用心得》

git clone git@github.com:ytchx1999/UCAS_DM_GTL_Tianchi.git
# create and checkout the branch
git checkout -b tianchi_v4
# or only checkout the branch
git checkout tianchi_v4
git checkout main
# pull from tianchi_v4 branch
git pull origin tianchi_v4:tianchi_v4

git add ...
git commit ...
git push -u origin tianchi_v4:tianchi_v4

Environment

torch == 1.6.0
pandas == 1.2.0
pickle == 0.7.5
wget == 3.2
torchsummary == 1.5.1
opencv == 4.5.3
PIL == 8.1.0
seaborn == 0.11.2

Hardwware:

  • CPU: skylake - Intel(R) Xeon(R) Gold 5118 CPU @ 2.30GHz
  • GPU: Tesla V100 (32GB)

Experiment Setup

Run corr analysis.

cd src/utils
python corr_csv.py

Run model.

cd src/utils
# processing .csv
python preprocess_csv.py
# load data
cd ..
python main.py
# or run in background
nohup python main.py > ../outputs/result.log 2>&1 &
# check result
tail -f ../outputs/result.log

Corr Analysis

heatmap by zhuhe

Pos_Corr (r>0.6)                      
 [('preVA', 'VA'),                     
  ('preIRF', 'IRF'),                   
  ('prePED', 'PED'),                   
  ('preHRF', 'HRF'),                   
  ('VA', 'preVA'),                     
  ('IRF', 'preIRF'),                   
  ('PED', 'prePED'),                   
  ('HRF', 'preHRF')]                   
                                        
Pos_Corr (r<-0.4)                     
 [('diagnosis', 'preSRF'),             
  ('diagnosis', 'prePED'),             
  ('diagnosis', 'continue injection'), 
  ('diagnosis', 'PED'),                
  ('preSRF', 'diagnosis'),             
  ('prePED', 'diagnosis'),             
  ('continue injection', 'diagnosis'), 
  ('PED', 'diagnosis')]                

Baseline模型结构

model

如图所示,模型(model.py)总共分为三个部分:黄色区域代表的上游主网络部分、绿色区域代表的上游辅助网络部分以及蓝色区域代表的下游网络部分。

Tricks

  • 图片特征增强:对训练集图片进行随机裁剪和翻转,测试集图片保持不变
  • 学习率调整:每10个epoch,学习率衰减0.1
  • 舍弃无用的特征属性(gender、age)
  • 对整形离散特征(diagnosis、anti-VEGF)进行encoding,得到低维的embedding
  • 预训练模型Resnet并冻结卷积层参数进行Fintune

不足

本次比赛基本达到了目标,仅使用最传统的ResNet模型进行模型搭建和设计,就成功进入了复赛。虽然如此,仍然有很多的不足之处:

  • 没有kfold cross validation和ensemble方法
  • 没有用到医学成像以及图像分割的domain knowledge和SOTA

Directory Structure

.
├── LICENSE
├── README.md
├── data
│   ├── APTOS\ Big\ Data\ Competition\ Preliminary\ Dataset.rtf
│   ├── Flowchart.png
│   ├── PreliminaryValidationSet_Info.csv
│   ├── TrainingAnnotation.csv
│   ├── data_train.ipynb
│   ├── resnet18-5c106cde.pth
│   ├── submit.csv
│   ├── test_data.pk
│   ├── train_data.pk
│   └── tree.txt
├── dataset
|   ├── split
│   ├── mix_train
│   │   ├── 0000-0000
│   │   │   ├── 0000-0000L_1.jpg
│   │   │   ├── 0000-0000L_2.jpg
│   │   │   ├── ...
│   │   └── ...
│   |── mix_test
|   ├── pkl_train
|   └── pkl_test 
└── src
    ├── __init__.py
    ├── main.py
    ├── models
    │   ├── __init__.py
    │   └── model.py
    └── utils
        ├── __init__.py
        ├── corr_csv.py
        ├── dataset.py
        ├── mix_images.py
        ├── preprocess_csv.py
        ├── transfer_img.py
        └── preprocess_img.py

About

2021 亚太眼科学会大数据竞赛(APTOS)。队伍:UCAS_DM_GTL,初赛rank: 85/10006。比赛官网:https://tianchi.aliyun.com/competition/entrance/531929/information

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