uc-berkeley-i-school / mids-210-radiology-triage-models-spring24

Models for Radiology Triage MIDS Capstone team spring 2024

Radiology Triage Capstone Models

AI models in development for the UC Berkeley Radiology Triage Capstone Project Spring 2024.

UC Berkeley

MIDS 210 Spring 2024

Authors: A Adam Saleh, Carolyn Dunlap, Cinthya Rosales, Esteban Valenzuela, Lee Gary, Steven Chang

Templated using Pytorch Project Template

This repo contains many files existing from the template. They remain as a point of reference during the initial setup of this project. It is expected that many of these files will change or be removed throughout the duration of this capstone.

Table of Contents:

• Radiology Triage Capstone Models
  • Table of Contents:
  • Tutorials:
  • Template Class Diagram:
  • Referenced Repos:
  • Repo Structure:
  • Requirements:

Tutorials:

We are providing a series of tutorials to get your started

• Getting Started Tutorial: where we provide a guide on the main steps to get started on your project.
• Mnist Tutorial: Here we take an already implemented NN model on Mnist and adapt it to our template structure.

Template Class Diagram:

Referenced Repos:

1. ERFNet: A model for Semantic Segmentation, trained on Pascal Voc
2. DCGAN: Deep Convolutional Generative Adverserial Networks, run on CelebA dataset.
3. CondenseNet: A model for Image Classification, trained on Cifar10 dataset
4. DQN: Deep Q Network model, a Reinforcement Learning example, tested on CartPole-V0
5. RecAE: Recurrent neural networks based autoencoder for Time series anomaly detection, run on ECG5000 dataset

Repo Structure:

After adding all our examples, the repo has the following structure:

├── agents
|  └── dcgan.py
|  └── condensenet.py
|  └── mnist.py
|  └── dqn.py
|  └── example.py
|  └── base.py
|  └── erfnet.py
|
├── configs
|  └── dcgan_exp_0.py
|  └── condensenet_exp_0.py
|  └── mnist_exp_0.py
|  └── dqn_exp_0.py
|  └── example_exp_0.py
|  └── erfnet_exp_0.py
|
├── data
|
├── datasets
|  └── cifar10.py
|  └── celebA.py
|  └── mnist.py
|  └── example.py
|  └── voc2012.py
|
├── experiments
|
├── graphs
|  └── models
|  |  └── custome_layers
|  |  |  └── denseblock.py
|  |  |  └── layers.py
|  |  |
|  |  └── dcgan_discriminator.py
|  |  └── dcgan_generator.py
|  |  └── erfnet.py
|  |  └── erfnet_imagenet.py
|  |  └── condensenet.py
|  |  └── mnist.py
|  |  └── dqn.py
|  |  └── example.py
|  |
|  └── losses
|  |  └── loss.py
|
├── pretrained_weights
|
├── tutorials
|
├── utils
|  └── assets
|
├── main.py
└── run.sh

Requirements:

easydict==1.7
graphviz==0.8.4
gym==0.10.5
imageio==2.3.0
matplotlib==2.2.2
numpy==1.14.5
Pillow==5.2.0
scikit-image==0.14.0
scikit-learn==0.19.1
scipy==1.1.0
tensorboardX==1.2
torch==0.4.0
torchvision==0.2.1
tqdm==4.23.4