Search Results for author: Joseph Bae

Found 13 papers, 3 papers with code

Token Sparsification for Faster Medical Image Segmentation

1 code implementation11 Mar 2023 Lei Zhou, Huidong Liu, Joseph Bae, Junjun He, Dimitris Samaras, Prateek Prasanna

To this end, we reformulate segmentation as a sparse encoding -> token completion -> dense decoding (SCD) pipeline.

Image Segmentation Medical Image Segmentation +2

Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation

no code implementations ICCV 2023 Aishik Konwer, Xiaoling Hu, Joseph Bae, Xuan Xu, Chao Chen, Prateek Prasanna

We propose a novel approach to learn enhanced modality-agnostic representations by employing a meta-learning strategy in training, even when only limited full modality samples are available.

Brain Tumor Segmentation Image Generation +4

Self Pre-training with Masked Autoencoders for Medical Image Classification and Segmentation

1 code implementation10 Mar 2022 Lei Zhou, Huidong Liu, Joseph Bae, Junjun He, Dimitris Samaras, Prateek Prasanna

Masked Autoencoder (MAE) has recently been shown to be effective in pre-training Vision Transformers (ViT) for natural image analysis.

Brain Tumor Segmentation Image Classification +4

Temporal Context Matters: Enhancing Single Image Prediction with Disease Progression Representations

no code implementations CVPR 2022 Aishik Konwer, Xuan Xu, Joseph Bae, Chao Chen, Prateek Prasanna

In our method, a self-attention based Temporal Convolutional Network (TCN) is used to learn a representation that is most reflective of the disease trajectory.

severity prediction

COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms

no code implementations21 Dec 2020 Rohan Sukumaran, Parth Patwa, T V Sethuraman, Sheshank Shankar, Rishank Kanaparti, Joseph Bae, Yash Mathur, Abhishek Singh, Ayush Chopra, Myungsun Kang, Priya Ramaswamy, Ramesh Raskar

In this study, we understand trends in the spread of COVID-19 by utilizing the results of self-reported COVID-19 symptoms surveys as an alternative to COVID-19 testing reports.

Time Series Forecasting

Predicting Clinical Outcomes in COVID-19 using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study

no code implementations15 Jul 2020 Joseph Bae, Saarthak Kapse, Gagandeep Singh, Rishabh Gattu, Syed Ali, Neal Shah, Colin Marshall, Jonathan Pierce, Tej Phatak, Amit Gupta, Jeremy Green, Nikhil Madan, Prateek Prasanna

Radiomic and DL classification models had mAUCs of 0. 78+/-0. 02 and 0. 81+/-0. 04, compared with expert scores mAUCs of 0. 75+/-0. 02 and 0. 79+/-0. 05 for mechanical ventilation requirement and mortality prediction, respectively.

Decision Making Mortality Prediction

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