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Found 8 papers, 1 papers with code
Annotation-cost Minimization for Medical Image Segmentation using Suggestive Mixed Supervision Fully Convolutional Networks
For medical image segmentation, most fully convolutional networks (FCNs) need strong supervision through a large sample of high-quality dense segmentations, which is taxing in terms of costs, time and logistics involved.
Cycle-Consistency for Robust Visual Question Answering
Despite significant progress in Visual Question Answering over the years, robustness of today's VQA models leave much to be desired.
Towards VQA Models That Can Read
We show that LoRRA outperforms existing state-of-the-art VQA models on our TextVQA dataset.
Ranked #3 on
Visual Question Answering (VQA)
on VizWiz 2018
A Hypersensitive Breast Cancer Detector
In this work, we leverage a large set of FFDM images with loose bounding boxes of mammographically significant findings to train a deep learning detector with extreme sensitivity.
Adaptation of a deep learning malignancy model from full-field digital mammography to digital breast tomosynthesis
In particular, we use average histogram matching (HM) and DL fine-tuning methods to generalize a FFDM model to the 2D maximum intensity projection (MIP) of DBT images.
LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion
In this paper, we present LiRaNet, a novel end-to-end trajectory prediction method which utilizes radar sensor information along with widely used lidar and high definition (HD) maps.
A deep learning algorithm for reducing false positives in screening mammography
This work lays the foundation for semi-autonomous breast cancer screening systems that could benefit patients and healthcare systems by reducing false positives, unnecessary procedures, patient anxiety, and expenses.
