Search Results for author:
Found 17 papers, 8 papers with code
Nucleus subtype classification using inter-modality learning
In this paper, we propose to use inter-modality learning to label previously un-labelable cell types on virtual H&E.
Inter-vendor harmonization of Computed Tomography (CT) reconstruction kernels using unpaired image translation
In this study, we adopt an unpaired image translation approach to investigate harmonization between and across reconstruction kernels from different manufacturers by constructing a multipath cycle generative adversarial network (GAN).
Scaling Up 3D Kernels with Bayesian Frequency Re-parameterization for Medical Image Segmentation
We hypothesize that convolution with LK sizes is limited to maintain an optimal convergence for locality learning.
UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation
Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical image analysis.
Body Composition Assessment with Limited Field-of-view Computed Tomography: A Semantic Image Extension Perspective
We evaluate the validity of the proposed method in automatic BC assessment using lung screening CT with limited FOV.
A Comparative Study of Confidence Calibration in Deep Learning: From Computer Vision to Medical Imaging
We believe that this study has merits to guide readers to choose calibration models and understand gaps between general computer vision and medical imaging domains.
Characterizing Renal Structures with 3D Block Aggregate Transformers
Efficiently quantifying renal structures can provide distinct spatial context and facilitate biomarker discovery for kidney morphology.
Low Pass Filter for Anti-aliasing in Temporal Action Localization
This paper aims to verify the existence of aliasing in TAL methods and investigate utilizing low pass filters to solve this problem by inhibiting the high-frequency band.
Mutual-Complementing Framework for Nuclei Detection and Segmentation in Pathology Image
The existing two unsupervised methods are prone to failure on degenerated samples.
SSD-GAN: Measuring the Realness in the Spatial and Spectral Domains
This issue makes the generator lack the incentive from the discriminator to learn high-frequency content of data, resulting in a significant spectrum discrepancy between generated images and real images.
RoIMix: Proposal-Fusion among Multiple Images for Underwater Object Detection
Generic object detection algorithms have proven their excellent performance in recent years.
Multi-mapping Image-to-Image Translation via Learning Disentanglement
Recent advances of image-to-image translation focus on learning the one-to-many mapping from two aspects: multi-modal translation and multi-domain translation.
ARMIN: Towards a More Efficient and Light-weight Recurrent Memory Network
In recent years, memory-augmented neural networks(MANNs) have shown promising power to enhance the memory ability of neural networks for sequential processing tasks.
BLP -- Boundary Likelihood Pinpointing Networks for Accurate Temporal Action Localization
Despite tremendous progress achieved in temporal action detection, state-of-the-art methods still suffer from the sharp performance deterioration when localizing the starting and ending temporal action boundaries.
SingleGAN: Image-to-Image Translation by a Single-Generator Network using Multiple Generative Adversarial Learning
Besides, we explore variants of SingleGAN for different tasks, including one-to-many domain translation, many-to-many domain translation and one-to-one domain translation with multimodality.
SEQUENCE MODELLING WITH AUTO-ADDRESSING AND RECURRENT MEMORY INTEGRATING NETWORKS
Processing sequential data with long term dependencies and learn complex transitions are two major challenges in many deep learning applications.
Multi-Mapping Image-to-Image Translation with Central Biasing Normalization
Recent advances in image-to-image translation have seen a rise in approaches generating diverse images through a single network.
