DLCV Final Project ( Medical-Imaging )

How to run your code?

To infer testing data

To reproduce the results, it is easy to run the provided script:

./omnipotent.sh <Path to Blood_test_data> <Path to the output csv file>

For example,

./omnipotent.sh Blood_data/test/ submit_test.csv

To train

STEP 1: train the CNN backbone

cd wubinray/base/
make train_resnet18

STEP 2: copy the CNN backbone model and fixed train/val set to the lstm folder

cp wubinary/base/checkpoints/resnet18/best.pth kaminyou/lstm/backbone_model/best.pth
cp wubinary/base/checkpoints/resnet18/train_set.pkl kaminyou/lstm/train_set.pkl
cp wubinary/base/checkpoints/resnet18/valid_set.pkl kaminyou/lstm/valid_set.pkl

STEP 3: revise the backbone model path and serialized train/val set path in config.yaml

PREPROCESSING:
  BACKBONE_MODEL_PATH: ./backbone_model/best.pth
LSTM_TRAINING: 
  TRAIN_SET_PATH: ./train_set.pkl
  VAL_SET_PATH: ./valid_set.pkl

STEP 4: train the LSTM model

python3 preprocessing.py
python3 train.py

STEP 5: obtain the lstm model

The lstm model will be saved in the kaminyou/lstm/experiment/datetime/lstm_best.pth.

STEP 6: infer for the testing data

Simply move those two models in the root folder and following the steps in previous To inference testing data part.

Usage

To start working on this final project, you should clone this repository into your local machine by using the following command:

git clone https://github.com/DLCV-Fall-2020/medical-imaging-<team_name>.git

Note that you should replace <team_name> with your own team name.

For more details, please click this link to view the slides of Final Project - Medical Imaging. Note that video and introduction pdf files for final project can be accessed in your NTU COOL.

Dataset

In the starter code of this repository, we have provided a shell script for downloading and extracting the dataset for this assignment. For Linux users, simply use the following command.

bash ./get_dataset.sh

The shell script will automatically download the dataset and store the data in a folder called Blood_data. Note that this command by default only works on Linux. If you are using other operating systems, you should download the dataset from this link and unzip the compressed file manually.

⚠️ IMPORTANT NOTE ⚠️

1. Please do not disclose the dataset! Also, do not upload your get_dataset.sh to your (public) Github.
2. You should keep a copy of the dataset only in your local machine. DO NOT upload the dataset to this remote repository. If you extract the dataset manually, be sure to put them in a folder called Blood_data under the root directory of your local repository so that it will be included in the default .gitignore file.

Evaluation

We will use F2-score to evaluate your model. Please refer to the introduction ppt for more details.

Submission Rules

Deadline

2021/1/22 11:59 GMT+8

Late Submission Policy

Late Submission is NOT allowed for final project!

Academic Honesty

• Taking any unfair advantages over other class members (or letting anyone do so) is strictly prohibited. Violating university policy would result in an F grade for this course (NOT negotiable).
• If you refer to some parts of the public code, you are required to specify the references in your report (e.g. URL to GitHub repositories).
• You are encouraged to discuss homework assignments with your fellow class members, but you must complete the assignment by yourself. TAs will compare the similarity of everyone’s submission. Any form of cheating or plagiarism will not be tolerated and will also result in an F grade for students with such misconduct.

🆕 NOTE
For the sake of conformity, please use the python3 command to call your .py files in all your shell scripts. Do not use python or other aliases, otherwise your commands may fail in our autograding scripts.

Packages

This homework should be done using python3.6. For a list of packages you are allowed to import in this assignment, please refer to requirments.txt for more details.

You can run the following command to install all the packages listed in requirements.txt:

pip3 install -r requirements.txt

Note that using packages with different versions will very likely lead to compatibility issues, so make sure that you install the correct version if one is specified above. E-mail or ask the TAs first if you want to import other packages.

Q&A

If you have any problems related to Final Project, you may

• Use TA hours
• Contact TAs by e-mail (ntudlcv@gmail.com)
• Post your question under Final Project FAQ section in FB group