Cross-domain few-shot segmentation (CD-FSS) aims to achieve semantic
seg...
Video colorization task has recently attracted wide attention. Recent me...
Weakly supervised semantic segmentation (WSSS) based on image-level labe...
For invasive breast cancer, immunohistochemical (IHC) techniques are oft...
In semi-supervised domain adaptation (SSDA), a few labeled target sample...
The divergence between labeled training data and unlabeled testing data ...
Explaining deep learning models is of vital importance for understanding...
Unsupervised domain adaptation (UDA) for semantic segmentation addresses...
The evaluation of human epidermal growth factor receptor 2 (HER2) expres...
Deep learning-based histopathology image classification is a key techniq...
In this paper, we propose a robust sample generation scheme to construct...
Objectives: To develop and validate a deep learning (DL)-based primary t...
Imperfect labels are ubiquitous in real-world datasets and seriously har...
Unsupervised person re-identification (Re-ID) is a promising and very
ch...
In recent years, deep learning-based methods have shown promising result...
It is very challenging for various visual tasks such as image fusion,
pe...
The divergence between labeled training data and unlabeled testing data ...
The automatic and objective medical diagnostic model can be valuable to
...
Segmentation from renal pathological images is a key step in automatic
a...
In this paper, we propose a novel method for highly efficient follicular...