Search Results for author:
Found 11 papers, 8 papers with code
AgileFormer: Spatially Agile Transformer UNet for Medical Image Segmentation
However, we argue that the current design of the vision transformer-based UNet (ViT-UNet) segmentation models may not effectively handle the heterogeneous appearance (e. g., varying shapes and sizes) of objects of interest in medical image segmentation tasks.
Ranked #2 on
Medical Image Segmentation
on ACDC
D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image Segmentation
D-Net is able to effectively utilize a multi-scale large receptive field and adaptively harness global contextual information.
SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classification
In this paper, we proposed a sparsely coded MIL (SC-MIL) that addresses those two aspects at the same time by leveraging sparse dictionary learning.
NSOTree: Neural Survival Oblique Tree
In this paper, we leverage the strengths of both neural networks and tree-based methods, capitalizing on their ability to approximate intricate functions while maintaining interpretability.
PDL: Regularizing Multiple Instance Learning with Progressive Dropout Layers
Multiple instance learning (MIL) was a weakly supervised learning approach that sought to assign binary class labels to collections of instances known as bags.
Multiple Instance Learning
Weakly Supervised Classification
+3
nnMobileNet: Rethinking CNN for Retinopathy Research
Over the past few decades, convolutional neural networks (CNNs) have been at the forefront of the detection and tracking of various retinal diseases (RD).
SC-VAE: Sparse Coding-based Variational Autoencoder with Learned ISTA
Several variants of variational autoencoders (VAEs) have been proposed to learn compact data representations by encoding high-dimensional data in a lower dimensional space.
TetCNN: Convolutional Neural Networks on Tetrahedral Meshes
Our results show the superiority of our LBO-based convolution layer and adapted pooling over the conventionally used unitary cortical thickness, graph Laplacian, and point cloud representation.
OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing
Non-mydriatic retinal color fundus photography (CFP) is widely available due to the advantage of not requiring pupillary dilation, however, is prone to poor quality due to operators, systemic imperfections, or patient-related causes.
Optimal Transport Guided Unsupervised Learning for Enhancing low-quality Retinal Images
In this paper, we proposed a simple but effective end-to-end framework for enhancing poor-quality retinal fundus images.
Self-Supervised Equivariant Regularization Reconciles Multiple Instance Learning: Joint Referable Diabetic Retinopathy Classification and Lesion Segmentation
Lesion appearance is a crucial clue for medical providers to distinguish referable diabetic retinopathy (rDR) from non-referable DR.
