dzcgaara / isic_2018_classification

Rank3 Code for ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection, Task 3

README

New!!: Code would be updated with using some latest approaches proposed in Nips/CVPR 2019. [2019/11/26]

New!!: Code has been updated with very basic settings. [2019/11/26]

New!!: Code and README would be updated very soon [2019/11/19]

Rank-3, MICCAI 2018 grand challenge "ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection", Task 3: "Lesion Diagnosis", 2018

Note: top 2 teams used additional public dataset to train models. However, our code don't use any extra data.

You can submit your prediction to liveboard now, if you want to find some better methods.

0. Directory

• src: contains all source codes
• scripts: bash scripts to train the model under different settings
• data: all images and csv file for splitting all data into training set and validation set.

. 
├── README.md
├── data
├── scripts
├── src
└── tags

You can download the dataset from Google Driver.

1. Run

You can run the code from top directory.

bash ./scripts/xxx.sh

2. Some ways to solve imbalanced data

1. median class weight

weight_sample_ = np.array([1113,6705,514,327,1099,115,142])/10015
weight_sample_ = 0.05132302/weight_sample_

2. Class weight

3. Training & Evaluation

See the scripts file.

tensorboard

mca, lr

tensorboard --logdir run