Search Results for author: Shih-Cheng Huang

Found 12 papers, 7 papers with code

BenchMD: A Benchmark for Modality-Agnostic Learning on Medical Images and Sensors

1 code implementation17 Apr 2023 Kathryn Wantlin, Chenwei Wu, Shih-Cheng Huang, Oishi Banerjee, Farah Dadabhoy, Veeral Vipin Mehta, Ryan Wonhee Han, Fang Cao, Raja R. Narayan, Errol Colak, Adewole Adamson, Laura Heacock, Geoffrey H. Tison, Alex Tamkin, Pranav Rajpurkar

Finally, we evaluate performance on out-of-distribution data collected at different hospitals than the training data, representing naturally-occurring distribution shifts that frequently degrade the performance of medical AI models.

Self-Supervised Learning

Video Pretraining Advances 3D Deep Learning on Chest CT Tasks

1 code implementation2 Apr 2023 Alexander Ke, Shih-Cheng Huang, Chloe P O'Connell, Michal Klimont, Serena Yeung, Pranav Rajpurkar

We demonstrate video pretraining improves the average performance of seven 3D models on two chest CT datasets, regardless of finetuning dataset size, and that video pretraining allows 3D models to outperform 2D baselines.

Image Classification

Adapting Pre-trained Vision Transformers from 2D to 3D through Weight Inflation Improves Medical Image Segmentation

1 code implementation8 Feb 2023 Yuhui Zhang, Shih-Cheng Huang, Zhengping Zhou, Matthew P. Lungren, Serena Yeung

Given the prevalence of 3D medical imaging technologies such as MRI and CT that are widely used in diagnosing and treating diverse diseases, 3D segmentation is one of the fundamental tasks of medical image analysis.

Image Segmentation Medical Image Segmentation +2

Diagnosing and Rectifying Vision Models using Language

1 code implementation8 Feb 2023 Yuhui Zhang, Jeff Z. HaoChen, Shih-Cheng Huang, Kuan-Chieh Wang, James Zou, Serena Yeung

Our proposed method can discover high-error data slices, identify influential attributes and further rectify undesirable model behaviors, without requiring any visual data.

Contrastive Learning

General Framework for Self-Supervised Model Priming for Parameter-Efficient Fine-tuning

no code implementations2 Dec 2022 Shih-Cheng Huang, Shih-Heng Wang, Min-Han Shih, Saurav Sahay, Hung-Yi Lee

To tackle these issues, we propose a general framework to enhance the few-shot adaptation and cross-domain generalization ability of parameter-efficient methods.

Domain Generalization

Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging

1 code implementation17 May 2022 Rui Yan, Liangqiong Qu, Qingyue Wei, Shih-Cheng Huang, Liyue Shen, Daniel Rubin, Lei Xing, Yuyin Zhou

The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder data sharing.

Federated Learning Privacy Preserving +2

RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR

no code implementations23 Nov 2021 Yuyin Zhou, Shih-Cheng Huang, Jason Alan Fries, Alaa Youssef, Timothy J. Amrhein, Marcello Chang, Imon Banerjee, Daniel Rubin, Lei Xing, Nigam Shah, Matthew P. Lungren

Despite the routine use of electronic health record (EHR) data by radiologists to contextualize clinical history and inform image interpretation, the majority of deep learning architectures for medical imaging are unimodal, i. e., they only learn features from pixel-level information.

Benchmarking Computed Tomography (CT) +2

Reading Race: AI Recognises Patient's Racial Identity In Medical Images

no code implementations21 Jul 2021 Imon Banerjee, Ananth Reddy Bhimireddy, John L. Burns, Leo Anthony Celi, Li-Ching Chen, Ramon Correa, Natalie Dullerud, Marzyeh Ghassemi, Shih-Cheng Huang, Po-Chih Kuo, Matthew P Lungren, Lyle Palmer, Brandon J Price, Saptarshi Purkayastha, Ayis Pyrros, Luke Oakden-Rayner, Chima Okechukwu, Laleh Seyyed-Kalantari, Hari Trivedi, Ryan Wang, Zachary Zaiman, Haoran Zhang, Judy W Gichoya

Methods: Using private and public datasets we evaluate: A) performance quantification of deep learning models to detect race from medical images, including the ability of these models to generalize to external environments and across multiple imaging modalities, B) assessment of possible confounding anatomic and phenotype population features, such as disease distribution and body habitus as predictors of race, and C) investigation into the underlying mechanism by which AI models can recognize race.

Ten Simple Rules for Reproducible Research in Jupyter Notebooks

2 code implementations13 Oct 2018 Adam Rule, Amanda Birmingham, Cristal Zuniga, Ilkay Altintas, Shih-Cheng Huang, Rob Knight, Niema Moshiri, Mai H. Nguyen, Sara Brin Rosenthal, Fernando Pérez, Peter W. Rose

For example, what are the technical and non-technical barriers to reproducible computational studies?

Other Computer Science Computers and Society

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