Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images.
Existing cell segmentation methods are often tailored to specific modalities or require …

Nuclear-derived morphological features and biomarkers provide relevant insights regarding
the tumour microenvironment, while also allowing diagnosis and prognosis in specific …

In computational pathology, automatic nuclei instance segmentation plays an essential role
in whole slide image analysis. While many computerized approaches have been proposed …

Despite advances in virological sciences and antiviral research, viruses continue to emerge,
circulate, and threaten public health. We still lack a comprehensive understanding of how …

Deep learning models have exhibited exceptional effectiveness in Computational Pathology
(CPath) by tackling intricate tasks across an array of histology image analysis applications …

Weakly supervised segmentation methods have garnered considerable attention due to
their potential to alleviate the need for labor-intensive pixel-level annotations during model …

Due to the increasing workload of pathologists, the need for automation to support
diagnostic tasks and quantitative biomarker evaluation is becoming more and more …

In cancer, a variety of cell types, along with their local density and spatial organization within
tissues, play a key role in driving cancer progression and modulating patient outcomes. At …

Cell segmentation is the fundamental task. Only by segmenting, can we define the
quantitative spatial unit for collecting measurements to draw biological conclusions. Deep …

Cervical cancer remains the fourth most common cancer among women worldwide. This
study proposes an end-to-end deep learning framework to predict consensus molecular …