From the 1 of 51 linked papers with an AI index.
2.2k citations · 7.8k across the 48 of their papers we have counts for
51 papers
Multimodal Deep Learning
Cem Akkus, Luyang Chu, Vladana Djakovic +14
This book is the result of a seminar in which we reviewed multimodal approaches and attempted to create a solid overview of the field, starting with the current state-of-the-art ap…
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…
CheXbreak: Misclassification Identification for Deep Learning Models Interpreting Chest X-rays
Emma Chen, Andy Kim, Rayan Krishnan +3
A major obstacle to the integration of deep learning models for chest x-ray interpretation into clinical settings is the lack of understanding of their failure modes. In this work,…
CheXseen: Unseen Disease Detection for Deep Learning Interpretation of Chest X-rays
Siyu Shi, Ishaan Malhi, Kevin Tran +2
We systematically evaluate the performance of deep learning models in the presence of diseases not labeled for or present during training. First, we evaluate whether deep learning…