The crux of semi-supervised semantic segmentation is to assign pseudo-labels to the pixels
of unlabeled images. A common practice is to select the highly confident predictions as the …

Tissue-level semantic segmentation is a vital step in computational pathology. Fully-
supervised models have already achieved outstanding performance with dense pixel-level …

Cervical cancer, as one of the most frequently diagnosed cancers worldwide, is curable
when detected early. Histopathology images play an important role in precision medicine of …

As one of the most important components of urban space, the geometric and semantic
properties of road trees are crucial for the assessment and upgrade of urban environments …

Learning from label proportions (LLP) is a promising weakly supervised learning problem. In
LLP, a set of instances (bag) has label proportions but no instance-level labels. LLP aims to …

Lung cancer is the leading cause of cancer death worldwide, and adenocarcinoma (LUAD)
is the most common subtype. Exploiting the potential value of the histopathology images can …

The crux of label-efficient semantic segmentation is to produce high-quality pseudo-labels to
leverage a large amount of unlabeled or weakly labeled data. A common practice is to select …

To cope with high annotation costs, training a classifier only from weakly supervised data
has attracted a great deal of attention these days. Among various approaches, strengthening …

Objective. Histopathology image segmentation can assist medical professionals in
identifying and diagnosing diseased tissue more efficiently. Although fully supervised …

While much of recent study in semi-supervised learning (SSL) has achieved strong
performance on single-label classification problems, an equally important yet underexplored …