Simple Summary The histopathological image is widely considered as the gold standard for
the diagnosis and prognosis of human cancers. Recently, deep learning technology has …

Single nucleus segmentation is a frequent challenge of microscopy image processing, since
it is the first step of many quantitative data analysis pipelines. The quality of tracking single …

A principal challenge in the analysis of tissue imaging data is cell segmentation—the task of
identifying the precise boundary of every cell in an image. To address this problem we …

Automated segmentation in medical image analysis is a challenging task that requires a
large amount of manually labeled data. However, most existing learning-based approaches …

Transformers have made remarkable progress towards modeling long-range dependencies
within the medical image analysis domain. However, current transformer-based models …

Circular extrachromosomal DNA (ecDNA) in patient tumors is an important driver of
oncogenic gene expression, evolution of drug resistance and poor patient outcomes …

Contrastive learning has shown great promise over annotation scarcity problems in the
context of medical image segmentation. Existing approaches typically assume a balanced …

An unresolved issue in contemporary biomedicine is the overwhelming number and
diversity of complex images that require annotation, analysis and interpretation. Recent …

Separating and labeling each nuclear instance (instance-aware segmentation) is the key
challenge in nuclear image segmentation. Deep Convolutional Neural Networks have been …

Many medical datasets have recently been created for medical image segmentation tasks,
and it is natural to question whether we can use them to sequentially train a single model …