Search Results for author:
Found 9 papers, 5 papers with code
RCdpia: A Renal Carcinoma Digital Pathology Image Annotation dataset based on pathologists
The annotation of digital pathological slide data for renal cell carcinoma is of paramount importance for correct diagnosis of artificial intelligence models due to the heterogeneous nature of the tumor.
Nucleus-aware Self-supervised Pretraining Using Unpaired Image-to-image Translation for Histopathology Images
Self-supervised pretraining attempts to enhance model performance by obtaining effective features from unlabeled data, and has demonstrated its effectiveness in the field of histopathology images.
Zero-shot Nuclei Detection via Visual-Language Pre-trained Models
Foremost, our work demonstrates that the VLPM pre-trained on natural image-text pairs exhibits astonishing potential for downstream tasks in the medical field as well.
Cyclic Learning: Bridging Image-level Labels and Nuclei Instance Segmentation
Experiments on three datasets demonstrate the good generality of our method, which outperforms other image-level weakly supervised methods for nuclei instance segmentation, and achieves comparable performance to fully-supervised methods.
Transformer based multiple instance learning for weakly supervised histopathology image segmentation
Hispathological image segmentation algorithms play a critical role in computer aided diagnosis technology.
Unsupervised Learning for Cell-level Visual Representation in Histopathology Images with Generative Adversarial Networks
The visual attributes of cells, such as the nuclear morphology and chromatin openness, are critical for histopathology image analysis.
Gland Instance Segmentation Using Deep Multichannel Neural Networks
Methods: We leverage the idea of image-to-image prediction in recent deep learning by designing an algorithm that automatically exploits and fuses complex multichannel information - regional, location, and boundary cues - in gland histology images.
Gland Instance Segmentation by Deep Multichannel Neural Networks
Here we leverage the idea of image-to-image prediction in recent deep learning by building a framework that automatically exploits and fuses complex multichannel information, regional, location and boundary patterns in gland histology images.
Gland Instance Segmentation by Deep Multichannel Side Supervision
In this paper, we propose a new image instance segmentation method that segments individual glands (instances) in colon histology images.
