6 accepted papers
Nuclei instance segmentation in histopathology images is essential for diagnostic accuracy and downstream computational tasks, yet this task relies heavily on expensive pixel level annotations. Although point level annotations substantially reduce the annotation burden for pathologists, many existin
Cancer is a leading cause of death worldwide due to its aggressive nature and complex variability. Accurate prognosis is therefore challenging but essential for guiding personalized treatment and follow-up. Previous research often relied on single data sources, missing the opportunity to combine var…
Image-level weakly supervised semantic segmentation (WSSS) reduces the dependence on high-quality data annotation, which plays a crucial role in computational pathology. Benefit from the ability to localize the objects with only binary labels, Class Activation Map (CAM) is a widely used method to in…
Nuclei segmentation is a fundamental and critical step in digital pathological image analysis. Fully supervised nuclei segmentation requires a lot of pixel-by-pixel manual annotation by pathologists, which is very time-consuming and laborious. To minimize the labeling burden of pathologists, this pa…
Accurate and automated gland segmentation on pathological images can assist pathologists in diagnosing the malignancy of colorectal adenocarcinoma. However, due to various gland shapes, severe deformation of malignant glands, and overlapping adhesions between glands. Gland segmentation has always be…
Vessel segmentation is essential for clinical diagnosis and surgical planning. However, it is quite challenging for automatic blood vessel segmentation due to low contrast, complex structure, and variable scale, especially when the annotated data is scarce. In this paper, we propose a novel multisca…