Search Results for author:
Found 17 papers, 5 papers with code
Evaluation and improvement of Segment Anything Model for interactive histopathology image segmentation
In the experimental results, SAM exhibits a weakness in segmentation performance compared to other models while demonstrating relative strengths in terms of inference time and generalization capability.
Reference-Free Isotropic 3D EM Reconstruction using Diffusion Models
Electron microscopy (EM) images exhibit anisotropic axial resolution due to the characteristics inherent to the imaging modality, presenting challenges in analysis and downstream tasks. In this paper, we propose a diffusion-model-based framework that overcomes the limitations of requiring reference data or prior knowledge about the degradation process.
CGAM: Click-Guided Attention Module for Interactive Pathology Image Segmentation via Backpropagating Refinement
Tumor region segmentation is an essential task for the quantitative analysis of digital pathology.
Histopathology Image Classification using Deep Manifold Contrastive Learning
Contrastive learning has gained popularity due to its robustness with good feature representation performance.
DiffMix: Diffusion Model-based Data Synthesis for Nuclei Segmentation and Classification in Imbalanced Pathology Image Datasets
The experimental results suggest that the proposed method improves the classification performance of the rare type nuclei classification, while showing superior segmentation and classification performance in imbalanced pathology nuclei datasets.
Scribble-supervised Cell Segmentation Using Multiscale Contrastive Regularization
The results show that the proposed multiscale contrastive loss is effective in improving the performance of S2L, which is comparable to that of the supervised learning segmentation method.
I2V: Towards Texture-Aware Self-Supervised Blind Denoising using Self-Residual Learning for Real-World Images
Although the advances of self-supervised blind denoising are significantly superior to conventional approaches without clean supervision in synthetic noise scenarios, it shows poor quality in real-world images due to spatially correlated noise corruption.
MitoVis: A Visually-guided Interactive Intelligent System for Neuronal Mitochondria Analysis
It is known that the morphology of mitochondria is closely related to the functions of neurons and neurodegenerative diseases.
ColorRL: Reinforced Coloring for End-to-End Instance Segmentation
Instance segmentation, the task of identifying and separating each individual object of interest in the image, is one of the actively studied research topics in computer vision.
RLCorrector: Reinforced Proofreading for Cell-level Microscopy Image Segmentation
Segmentation of nanoscale electron microscopy (EM) images is crucial but still challenging in connectomics research.
ISCL: Interdependent Self-Cooperative Learning for Unpaired Image Denoising
With the advent of advances in self-supervised learning, paired clean-noisy data are no longer required in deep learning-based image denoising.
Noise2Kernel: Adaptive Self-Supervised Blind Denoising using a Dilated Convolutional Kernel Architecture
In this paper, we propose a dilated convolutional network that satisfies an invariant property, allowing efficient kernel-based training without random masking.
Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistency
Segmentation is a fundamental process in microscopic cell image analysis.
Reinforced Coloring for End-to-End Instance Segmentation
To exploit the advantages of conventional single-object-per-step segmentation methods without impairing the scalability, we propose a novel iterative deep reinforcement learning agent that learns how to differentiate multiple objects in parallel.
Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Loss
In this paper, we propose a novel deep learning-based generative adversarial model, RefineGAN, for fast and accurate CS-MRI reconstruction.
ssEMnet: Serial-section Electron Microscopy Image Registration using a Spatial Transformer Network with Learned Features
The alignment of serial-section electron microscopy (ssEM) images is critical for efforts in neuroscience that seek to reconstruct neuronal circuits.
FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics
Electron microscopic connectomics is an ambitious research direction with the goal of studying comprehensive brain connectivity maps by using high-throughput, nano-scale microscopy.
