126 citations · 130 across the 7 of their papers we have counts for
8 papers
Predicting Early-Onset Colorectal Cancer with Large Language Models
Wilson Lau, Youngwon Kim, Sravanthi Parasa +3
The incidence rate of early-onset colorectal cancer (EoCRC, age < 45) has increased every year, but this population is younger than the recommended age established by national guid…
Mask-conditioned latent diffusion for generating gastrointestinal polyp images
Roman Macháček, Leila Mozaffari, Zahra Sepasdar +4
In order to take advantage of AI solutions in endoscopy diagnostics, we must overcome the issue of limited annotations. These limitations are caused by the high privacy concerns in…
PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps
Jan Andre Fagereng, Vajira Thambawita, Andrea M. Storås +4
Early identification of a polyp in the lower gastrointestinal (GI) tract can lead to prevention of life-threatening colorectal cancer. Developing computer-aided diagnosis (CAD) sys…
Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations
Steven Hicks, Andrea Storås, Michael Riegler +6
Deep learning has in recent years achieved immense success in all areas of computer vision and has the potential of assisting medical doctors in analyzing visual content for diseas…
Literature-Augmented Clinical Outcome Prediction
Aakanksha Naik, Sravanthi Parasa, Sergey Feldman +2
We present BEEP (Biomedical Evidence-Enhanced Predictions), a novel approach for clinical outcome prediction that retrieves patient-specific medical literature and incorporates it…
A Search Engine for Discovery of Scientific Challenges and Directions
Dan Lahav, Jon Saad Falcon, Bailey Kuehl +8
Keeping track of scientific challenges, advances and emerging directions is a fundamental part of research. However, researchers face a flood of papers that hinders discovery of im…
SinGAN-Seg: Synthetic training data generation for medical image segmentation
Vajira Thambawita, Pegah Salehi, Sajad Amouei Sheshkal +6
Analyzing medical data to find abnormalities is a time-consuming and costly task, particularly for rare abnormalities, requiring tremendous efforts from medical experts. Artificial…
MedICaT: A Dataset of Medical Images, Captions, and Textual References
Sanjay Subramanian, Lucy Lu Wang, Sachin Mehta +6
Understanding the relationship between figures and text is key to scientific document understanding. Medical figures in particular are quite complex, often consisting of several su…