F18-STAT841-Proposal
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Project # 0 Group members:
Last name, First name
Last name, First name
Last name, First name
Last name, First name
Title: Making a String Telephone
Description: We use paper cups to make a string phone and talk with friends while learning about sound waves with this science project. (Explain your project in one or two paragraphs).
Project # 1 Group members:
Weng, Jiacheng
Li, Keqi
Qian, Yi
Liu, Bomeng
Title: RSNA Pneumonia Detection Challenge
Description:
Our team’s project is the RSNA Pneumonia Detection Challenge from Kaggle competition. The primary goal of this project is to develop a machine learning tool to detect patients with pneumonia based on their chest radiographs (CXR).
Pneumonia is an infection that inflames the air sacs in human lungs which has symptoms such as chest pain, cough, and fever [1]. Pneumonia can be very dangerous especially to infants and elders. In 2015, 920,000 children under the age of 5 died from this disease [2]. Due to its fatality to children, diagnosing pneumonia has a high order. A common method of diagnosing pneumonia is to obtain patients’ chest radiograph (CXR) which is a gray-scale scan image of patients’ chests using x-ray. The infected region due to pneumonia usually shows as an area or areas of increased opacity [3] on CXR. However, many other factors can also contribute to increase in opacity on CXR which makes the diagnose very challenging. The diagnose also requires highly-skilled clinicians and a lot of time of CXR screening. The Radiological Society of North America (RSNA®) sees the opportunity of using machine learning to potentially accelerate the initial CXR screening process.
For the scope of this project, our team plans to contribute to solving this problem by applying our machine learning knowledge in image processing and classification. Team members are going to apply techniques that include, but are not limited to: logistic regression, random forest, SVM, kNN, CNN, etc., in order to successfully detect CXRs with pneumonia.
[1] (Accessed 2018, Oct. 4). Pneumonia [Online]. MAYO CLINIC. Available from: https://www.mayoclinic.org/diseases-conditions/pneumonia/symptoms-causes/syc-20354204
[2] (Accessed 2018, Oct. 4). RSNA Pneumonia Detection Challenge [Online]. Kaggle. Available from: https://www.kaggle.com/c/rsna-pneumonia-detection-challenge
[3] Franquet T. Imaging of community-acquired pneumonia. J Thorac Imaging 2018 (epub ahead of print). PMID 30036297
Project # 2 Group members:
Hou, Zhaoran
Zhang, Chi
Title:
Description:
Project # 3 Group members:
Hanzhen Yang
Jing Pu Sun
Ganyuan Xuan
Yu Su
Title: Kaggle Challenge: Quick, Draw! Doodle Recognition Challenge
Description:
Our team chose the Quick, Draw! Doodle Recognition Challenge from the Kaggle Competition. The goal of the competition is to build an image recognition tool that can classify hand-drawn doodles into one of the 340 categories.
The main challenge of the project remains in the training set being very noisy. Hand-drawn artwork may deviate substantially from the actual object, and is almost definitively different from person to person. Mislabeled images also present a problem since they will create outlier points when we train our models.
We plan on learning more about some of the currently mature image recognition algorithms to inspire and develop our own model.
Project # 4 Group members:
Snaith, Mitchell
Title: Reproducibility report: *Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks*
Description:
The paper *Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks* [1] has been submitted to ICLR 2019. It aims to "fix" variational Bayes and turn it into a robust inference tool through two innovations.
Goals are to:
- reproduce the deterministic variational inference scheme as described in the paper without referencing the original author's code, providing a 3rd party implementation
- reproduce experiment results with own implementation, using the same NN framework for reference implementations of compared methods described in the paper
- reproduce experiment results with the author's own implementation
- explore other possible applications of variational Bayes besides heteroscedastic regression
[1] OpenReview location: https://openreview.net/forum?id=B1l08oAct7