39 citations · 67 across the 5 of their papers we have counts for
5 papers
Effect of Radiology Report Labeler Quality on Deep Learning Models for Chest X-Ray Interpretation
Saahil Jain, Akshay Smit, Andrew Y. Ng +1
Although deep learning models for chest X-ray interpretation are commonly trained on labels generated by automatic radiology report labelers, the impact of improvements in report l…
MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning
Akshay Smit, Damir Vrabac, Yujie He +3
We propose a selective learning method using meta-learning and deep reinforcement learning for medical image interpretation in the setting of limited labeling resources. Our method…
VisualCheXbert: Addressing the Discrepancy Between Radiology Report Labels and Image Labels
Saahil Jain, Akshay Smit, Steven QH Truong +7
Automatic extraction of medical conditions from free-text radiology reports is critical for supervising computer vision models to interpret medical images. In this work, we show th…
DLBCL-Morph: Morphological features computed using deep learning for an annotated digital DLBCL image set
Damir Vrabac, Akshay Smit, Rebecca Rojansky +5
Diffuse Large B-Cell Lymphoma (DLBCL) is the most common non-Hodgkin lymphoma. Though histologically DLBCL shows varying morphologies, no morphologic features have been consistentl…
CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT
Akshay Smit, Saahil Jain, Pranav Rajpurkar +3
The extraction of labels from radiology text reports enables large-scale training of medical imaging models. Existing approaches to report labeling typically rely either on sophist…