12 citations · 26 across the 9 of their papers we have counts for
9 papers
ConvTransSeg: A Multi-resolution Convolution-Transformer Network for Medical Image Segmentation
Zhendi Gong, Andrew P. French, Guoping Qiu +1
Convolutional neural networks (CNNs) achieved the state-of-the-art performance in medical image segmentation due to their ability to extract highly complex feature representations.…
Two-Stream UNET Networks for Semantic Segmentation in Medical Images
Xin Chen, Ke Ding
Recent advances of semantic image segmentation greatly benefit from deeper and larger Convolutional Neural Network (CNN) models. Compared to image segmentation in the wild, propert…
Multi-Layer Pseudo-Supervision for Histopathology Tissue Semantic Segmentation using Patch-level Classification Labels
Chu Han, Jiatai Lin, Jinhai Mai +15
Tissue-level semantic segmentation is a vital step in computational pathology. Fully-supervised models have already achieved outstanding performance with dense pixel-level annotati…
Image Augmentation Using a Task Guided Generative Adversarial Network for Age Estimation on Brain MRI
Ruizhe Li, Matteo Bastiani, Dorothee Auer +2
Brain age estimation based on magnetic resonance imaging (MRI) is an active research area in early diagnosis of some neurodegenerative diseases (e.g. Alzheimer, Parkinson, Huntingt…
FU-net: Multi-class Image Segmentation Using Feedback Weighted U-net
Mina Jafari, Ruizhe Li, Yue Xing +4
In this paper, we present a generic deep convolutional neural network (DCNN) for multi-class image segmentation. It is based on a well-established supervised end-to-end DCNN model,…
DRU-net: An Efficient Deep Convolutional Neural Network for Medical Image Segmentation
Mina Jafari, Dorothee Auer, Susan Francis +2
Residual network (ResNet) and densely connected network (DenseNet) have significantly improved the training efficiency and performance of deep convolutional neural networks (DCNNs)…