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Found 5 papers, 2 papers with code
Ord2Seq: Regarding Ordinal Regression as Label Sequence Prediction
In this way, we decompose an ordinal regression task into a series of recursive binary classification steps, so as to subtly distinguish adjacent categories.
Robust Image Ordinal Regression with Controllable Image Generation
Image ordinal regression has been mainly studied along the line of exploiting the order of categories.
CTT-Net: A Multi-view Cross-token Transformer for Cataract Postoperative Visual Acuity Prediction
Clinically, to assess the necessity of cataract surgery, accurately predicting postoperative VA before surgery by analyzing multi-view optical coherence tomography (OCT) images is crucially needed.
Globally Optimal Segmentation of Mutually Interacting Surfaces using Deep Learning
In this work, we propose to parameterize the surface cost functions in the graph model and leverage DL to learn those parameters.
Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation
Unlike existing litera- ture on quantization which primarily targets memory and computation complexity reduction, we apply quan- tization as a method to reduce over tting in FCNs for better accuracy.
