Search Results for author:
Found 25 papers, 14 papers with code
UniCell: Universal Cell Nucleus Classification via Prompt Learning
The recognition of multi-class cell nuclei can significantly facilitate the process of histopathological diagnosis.
Cell Graph Transformer for Nuclei Classification
Nuclei classification is a critical step in computer-aided diagnosis with histopathology images.
nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Model
Extensive experiments on 6 datasets demonstrate nnMamba's superiority over state-of-the-art methods in a suite of challenging tasks, including 3D image segmentation, classification, and landmark detection.
Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images
Afterward, we train a separate segmentation model for each category using the images in the corresponding category.
Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection
Multi-class cell nuclei detection is a fundamental prerequisite in the diagnosis of histopathology.
Visual-Attribute Prompt Learning for Progressive Mild Cognitive Impairment Prediction
Furthermore, we propose a Prompt fine-Tuning (PT) scheme to transfer the knowledge from AD prediction task for progressive MCI (pMCI) diagnosis.
ASC: Appearance and Structure Consistency for Unsupervised Domain Adaptation in Fetal Brain MRI Segmentation
To address the task, we propose a new UDA framework based on Appearance and Structure Consistency, named ASC.
Prompt-based Grouping Transformer for Nucleus Detection and Classification
Automatic nuclei detection and classification can produce effective information for disease diagnosis.
Diffusion-based Data Augmentation for Nuclei Image Segmentation
Therefore, augmenting a dataset with only a few labeled images to improve the segmentation performance is of significant research and application value.
The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions
This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.
Structure Embedded Nucleus Classification for Histopathology Images
Next, we convert a histopathology image into a graph structure with nuclei as nodes, and build a graph neural network to embed the spatial distribution of nuclei into their representations.
Which Pixel to Annotate: a Label-Efficient Nuclei Segmentation Framework
Recently deep neural networks, which require a large amount of annotated samples, have been widely applied in nuclei instance segmentation of H\&E stained pathology images.
View-Disentangled Transformer for Brain Lesion Detection
Deep neural networks (DNNs) have been widely adopted in brain lesion detection and segmentation.
Attentive Symmetric Autoencoder for Brain MRI Segmentation
Self-supervised learning methods based on image patch reconstruction have witnessed great success in training auto-encoders, whose pre-trained weights can be transferred to fine-tune other downstream tasks of image understanding.
Robust Real-World Image Super-Resolution against Adversarial Attacks
Since the frequency masking may not only destroys the adversarial perturbations but also affects the sharp details in a clean image, we further develop an adversarial sample classifier based on the frequency domain of images to determine if applying the proposed mask module.
BronchusNet: Region and Structure Prior Embedded Representation Learning for Bronchus Segmentation and Classification
CT-based bronchial tree analysis plays an important role in the computer-aided diagnosis for respiratory diseases, as it could provide structured information for clinicians.
SSMD: Semi-Supervised Medical Image Detection with Adaptive Consistency and Heterogeneous Perturbation
Semi-Supervised classification and segmentation methods have been widely investigated in medical image analysis.
Online Alternate Generator against Adversarial Attacks
The field of computer vision has witnessed phenomenal progress in recent years partially due to the development of deep convolutional neural networks.
Depthwise Non-local Module for Fast Salient Object Detection Using a Single Thread
The proposed algorithm for the first time achieves competitive accuracy and high inference efficiency simultaneously with a single CPU thread.
Motion Guided Attention for Video Salient Object Detection
In this paper, we develop a multi-task motion guided video salient object detection network, which learns to accomplish two sub-tasks using two sub-networks, one sub-network for salient object detection in still images and the other for motion saliency detection in optical flow images.
ROSA: Robust Salient Object Detection against Adversarial Attacks
To our knowledge, this paper is the first one that mounts successful adversarial attacks on salient object detection models and verifies that adversarial samples are effective on a wide range of existing methods.
Non-Local Context Encoder: Robust Biomedical Image Segmentation against Adversarial Attacks
In this paper, we discover that global spatial dependencies and global contextual information in a biomedical image can be exploited to defend against adversarial attacks.
Harvesting Visual Objects from Internet Images via Deep Learning Based Objectness Assessment
As a concrete example, a database of over 1. 2 million visual objects has been built using the proposed method, and has been successfully used in various data-driven image applications.
Context-Aware Semantic Inpainting
Our proposed GAN-based framework consists of a fully convolutional design for the generator which helps to better preserve spatial structures and a joint loss function with a revised perceptual loss to capture high-level semantics in the context.
