7 papers · 1 filter
Merlin: A Computed Tomography Vision-Language Foundation Model and Dataset
Louis Blankemeier, Ashwin Kumar, Joseph Paul Cohen +37
The large volume of abdominal computed tomography (CT) scans coupled with the shortage of radiologists have intensified the need for automated medical image analysis tools. Previou…
Comp2Comp: Open-Source Software with FDA-Cleared Artificial Intelligence Algorithms for Computed Tomography Image Analysis
Adrit Rao, Malte Jensen, Andrea T. Fisher +28
Artificial intelligence allows automatic extraction of imaging biomarkers from already-acquired radiologic images. This paradigm of opportunistic imaging adds value to medical imag…
Automated detection of underdiagnosed medical conditions via opportunistic imaging
Asad Aali, Andrew Johnston, Louis Blankemeier +6
Abdominal computed tomography (CT) scans are frequently performed in clinical settings. Opportunistic CT involves repurposing routine CT images to extract diagnostic information an…
Explaining 3D Computed Tomography Classifiers with Counterfactuals
Joseph Paul Cohen, Louis Blankemeier, Akshay Chaudhari
Counterfactual explanations enhance the interpretability of deep learning models in medical imaging, yet adapting them to 3D CT scans poses challenges due to volumetric complexity…
Time-to-Event Pretraining for 3D Medical Imaging
Zepeng Huo, Jason Alan Fries, Alejandro Lozano +6
With the rise of medical foundation models and the growing availability of imaging data, scalable pretraining techniques offer a promising way to identify imaging biomarkers predic…
Identifying Spurious Correlations using Counterfactual Alignment
Joseph Paul Cohen, Louis Blankemeier, Akshay Chaudhari
Models driven by spurious correlations often yield poor generalization performance. We propose the counterfactual (CF) alignment method to detect and quantify spurious correlations…