Search Results for author:
Found 10 papers, 5 papers with code
Multi-rater Prompting for Ambiguous Medical Image Segmentation
In this paper, we tackle two challenges arisen in multi-rater annotations for medical image segmentation (called ambiguous medical image segmentation): (1) How to train a deep learning model when a group of raters produces a set of diverse but plausible annotations, and (2) how to fine-tune the model efficiently when computation resources are not available for re-training the entire model on a different dataset domain.
PoCo: A Self-Supervised Approach via Polar Transformation Based Progressive Contrastive Learning for Ophthalmic Disease Diagnosis
In this paper, we present a self-supervised method via polar transformation based progressive contrastive learning, called PoCo, for ophthalmic disease diagnosis.
Large Window-based Mamba UNet for Medical Image Segmentation: Beyond Convolution and Self-attention
In this paper, we introduce a Large Window-based Mamba U}-shape Network, or LMa-UNet, for 2D and 3D medical image segmentation.
Jointly Explicit and Implicit Cross-Modal Interaction Network for Anterior Chamber Inflammation Diagnosis
To this end, we propose a jointly Explicit and implicit Cross-Modal Interaction Network (EiCI-Net) for Anterior Chamber Inflammation Diagnosis that uses anterior segment optical coherence tomography (AS-OCT) images, slit-lamp images, and clinical data jointly.
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.
Classification of Single Tree Decay Stages from Combined Airborne LiDAR Data and CIR Imagery
Finally, the classification is conducted on the two datasets (3D multispectral point clouds and 2D projected images) based on the three Machine Learning algorithms.
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.
D-Former: A U-shaped Dilated Transformer for 3D Medical Image Segmentation
In this paper, we propose a new method called Dilated Transformer, which conducts self-attention for pair-wise patch relations captured alternately in local and global scopes.
Calibration of Human Driving Behavior and Preference Using Naturalistic Traffic Data
Towards this end it is necessary that we have a comprehensive modeling framework for decision-making within which human driving preferences can be inferred statistically from observed driving behaviors in realistic and naturalistic traffic settings.
