Deep convolution neural networks have been widely used in medical image analysis, such
as lesion identification in whole-slide images, cancer detection, and cell segmentation, etc …

Tissue/region segmentation of pathology images is essential for quantitative analysis in
digital pathology. Previous studies usually require full supervision (eg, pixel-level …

Deep learning for cell instance segmentation is a significant research direction in biomedical
image analysis. The traditional supervised learning methods rely on pixel-wise annotation of …

We propose weakly supervised training schemes to train end-to-end cell segmentation
networks that only require a single point annotation per cell as the training label and …

Deep convolutional neural networks have been highly effective in segmentation tasks.
However, segmentation becomes more difficult when training images include many complex …

In this paper, we propose a weakly supervised deep convolutional neural network for
medical image segmentation using an uncertainty guided and spatially constrained loss …

Cell nuclei segmentation is important for histopathology image analysis. While deep
learning has demonstrated promising results for automated cell nuclei segmentation, it is …

Image segmentation is a fundamental task in the field of imaging and vision. Supervised
deep learning for segmentation has achieved unparalleled success when sufficient training …

Segmentation using deep learning has shown promising directions in medical imaging as it
aids in the analysis and diagnosis of diseases. Nevertheless, a main drawback of deep …

Consistency regularization and pseudo labeling-based semi-supervised methods perform
co-training using the pseudo labels from multi-view inputs. However, such co-training …