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Found 12 papers, 2 papers with code
Exploring Test-Time Adaptation for Object Detection in Continually Changing Environments
We demonstrate the effectiveness of AMROD on four CTTA object detection tasks, where AMROD outperforms existing methods, especially achieving a 3. 2 mAP improvement and a 20% increase in efficiency on the Cityscapes-to-Cityscapes-C CTTA task.
A$^{2}$-MAE: A spatial-temporal-spectral unified remote sensing pre-training method based on anchor-aware masked autoencoder
A$^{2}$-MAE integrates an anchor-aware masking strategy and a geographic encoding module to comprehensively exploit the properties of RS images.
Learning Shape Priors by Pairwise Comparison for Robust Semantic Segmentation
A cosine similarity based attention module is proposed to fuse the information from both encoders, to utilize both types of prior information encoded by the template-encoder and model the inter-subject similarity for each foreground class.
Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network
Based on the aforementioned innovations, we achieve state-of-the-art results on the MICCAI 2017 Liver Tumor Segmentation (LiTS) dataset.
RECIST-Net: Lesion detection via grouping keypoints on RECIST-based annotation
Universal lesion detection in computed tomography (CT) images is an important yet challenging task due to the large variations in lesion type, size, shape, and appearance.
Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation
In this paper, we show that such process can be integrated into the one-shot segmentation task which is a very challenging but meaningful topic.
Brain Atlas Guided Attention U-Net for White Matter Hyperintensity Segmentation
In this paper, we propose a novel brain atlas guided attention U-Net (BAGAU-Net) that leverages only FLAIR images with a spatially-registered white matter (WM) brain atlas to yield competitive WMH segmentation performance.
Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks
We propose a Healthcare Graph Convolutional Network (HealGCN) to offer disease self-diagnosis service for online users based on Electronic Healthcare Records (EHRs).
Superpixel-Guided Label Softening for Medical Image Segmentation
If a superpixel intersects with the annotation boundary, we consider a high probability of uncertain labeling within this area.
Learning and Exploiting Interclass Visual Correlations for Medical Image Classification
In this paper, we present the Class-Correlation Learning Network (CCL-Net) to learn interclass visual correlations from given training data, and produce soft labels to help with classification tasks.
LT-Net: Label Transfer by Learning Reversible Voxel-wise Correspondence for One-shot Medical Image Segmentation
We introduce a one-shot segmentation method to alleviate the burden of manual annotation for medical images.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
