42 citations · 103 across the 8 of their papers we have counts for
9 papers
A Reasoning-Enabled Vision-Language Foundation Model for Chest X-ray Interpretation
Yabin Zhang, Chong Wang, Yunhe Gao +19
Chest X-rays (CXRs) are among the most frequently performed imaging examinations worldwide, yet rising imaging volumes increase radiologist workload and the risk of diagnostic erro…
Visual concept ranking uncovers medical shortcuts used by large multimodal models
Joseph D. Janizek, Sonnet Xu, Junayd Lateef +1
Ensuring the reliability of machine learning models in safety-critical domains such as healthcare requires auditing methods that can uncover model shortcomings. We introduce a meth…
BiasICL: In-Context Learning and Demographic Biases of Vision Language Models
Sonnet Xu, Joseph Janizek, Yixing Jiang +1
Vision language models (VLMs) show promise in medical diagnosis, but their performance across demographic subgroups when using in-context learning (ICL) remains poorly understood.…
LAB-Bench: Measuring Capabilities of Language Models for Biology Research
Jon M. Laurent, Joseph D. Janizek, Michael Ruzo +6
There is widespread optimism that frontier Large Language Models (LLMs) and LLM-augmented systems have the potential to rapidly accelerate scientific discovery across disciplines.…
True to the Model or True to the Data?
Hugh Chen, Joseph D. Janizek, Scott Lundberg +1
A variety of recent papers discuss the application of Shapley values, a concept for explaining coalitional games, for feature attribution in machine learning. However, the correct…
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks
Joseph D. Janizek, Pascal Sturmfels, Su-In Lee
Recent work has shown great promise in explaining neural network behavior. In particular, feature attribution methods explain which features were most important to a model's predic…
An Adversarial Approach for the Robust Classification of Pneumonia from Chest Radiographs
Joseph D. Janizek, Gabriel Erion, Alex J. DeGrave +1
While deep learning has shown promise in the domain of disease classification from medical images, models based on state-of-the-art convolutional neural network architectures often…
Learning Deep Attribution Priors Based On Prior Knowledge
Ethan Weinberger, Joseph Janizek, Su-In Lee
Feature attribution methods, which explain an individual prediction made by a model as a sum of attributions for each input feature, are an essential tool for understanding the beh…
Improving performance of deep learning models with axiomatic attribution priors and expected gradients
Gabriel Erion, Joseph D. Janizek, Pascal Sturmfels +2
Recent research has demonstrated that feature attribution methods for deep networks can themselves be incorporated into training; these attribution priors optimize for a model whos…