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Found 6 papers, 1 papers with code
Natural Adversarial Objects
Moreover, by comparing a variety of object detection architectures, we find that better performance on MSCOCO validation set does not necessarily translate to better performance on NAO, suggesting that robustness cannot be simply achieved by training a more accurate model.
Ranked #1 on
Object Detection
on NAO
On The State of Data In Computer Vision: Human Annotations Remain Indispensable for Developing Deep Learning Models
High-quality labeled datasets play a crucial role in fueling the development of machine learning (ML), and in particular the development of deep learning (DL).
Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset
We train a deep neural network model to classify 114 skin conditions and find that the model is most accurate on skin types similar to those it was trained on.
ScarGAN: Chained Generative Adversarial Networks to Simulate Pathological Tissue on Cardiovascular MR Scans
Medical images with specific pathologies are scarce, but a large amount of data is usually required for a deep convolutional neural network (DCNN) to achieve good accuracy.
Computationally efficient cardiac views projection using 3D Convolutional Neural Networks
We demonstrate that the long and short axis projections computed with our automated method are of equivalent quality to projections created with landmarks placed by an experienced cardiac radiologist, based on a blinded test administered to a different cardiac radiologist.
FastVentricle: Cardiac Segmentation with ENet
Cardiac Magnetic Resonance (CMR) imaging is commonly used to assess cardiac structure and function.
