Rohan Doshi

Walt Williams, Rohan Doshi, Yanran Li et al.

Heart failure remains a major public health challenge with growing costs. Ejection fraction (EF) is a key metric for the diagnosis and management of heart failure however estimation of EF using echocardiography remains expensive for the healthcare system and subject to intra/inter operator variability. While chest x-rays (CXR) are quick, inexpensive, and require less expertise, they do not provide sufficient information to the human eye to estimate EF. This work explores the efficacy of computer vision techniques to predict reduced EF solely from CXRs. We studied a dataset of 3488 CXRs from the MIMIC CXR-jpg (MCR) dataset. Our work establishes benchmarks using multiple state-of-the-art convolutional neural network architectures. The subsequent analysis shows increasing model sizes from 8M to 23M parameters improved classification performance without overfitting the dataset. We further show how data augmentation techniques such as CXR rotation and random cropping further improves model performance another ~5%. Finally, we conduct an error analysis using saliency maps and Grad-CAMs to better understand the failure modes of convolutional models on this task.

Prem Nair, Rohan Doshi, Stefan Keselj

Convolutional neural networks use pooling and other downscaling operations to maintain translational invariance for detection of features, but in their architecture they do not explicitly maintain a representation of the locations of the features relative to each other. This means they do not represent two instances of the same object in different orientations the same way, like humans do, and so training them often requires extensive data augmentation and exceedingly deep networks. A team at Google Brain recently made news with an attempt to fix this problem: Capsule Networks. While a normal CNN works with scalar outputs representing feature presence, a CapsNet works with vector outputs representing entity presence. We want to stress test CapsNet in various incremental ways to better understand their performance and expressiveness. In broad terms, the goals of our investigation are: (1) test CapsNets on datasets that are like MNIST but harder in a specific way, and (2) explore the internal embedding space and sources of error for CapsNets.