6 accepted papers
Tissue segmentation in pathology images is crucial for computer-aided diagnostics of human cancers. Traditional tissue segmentation models rely heavily on large-scale labeled datasets, where every tissue type must be annotated by experts. However, due to the complexity of tumor micro-environment, co…
Conventional single-dataset training often fails with new data distributions, especially in ultrasound (US) image analysis due to limited data, acoustic shadows, and speckle noise.Therefore, constructing a universal framework for multi-heterogeneous US datasets is imperative. However, a key challeng…
Prompt learning has emerged as a promising paradigm for adapting pre-trained vision-language models (VLMs) to few-shot whole slide image (WSI) classification by aligning visual features with textual representations, thereby reducing annotation cost and enhancing model generalization. Nevertheless, e…
The rapid development of spatial transcriptomics (ST) allows researchers to measure the spatial-level gene expression in tissues. Although powerful, the cost for collecting the ST data is expensive, and thus several studies aim to predict gene expression in ST by utilizing their corresponding H/E st…
The integrative analysis of histopathological images and genomic data has received increasing attention for survival prediction of human cancers. However, the existing studies always hold the assumption that full modalities are available. As a matter of fact, the cost for collecting genomic data is…
The recent advance of deep learning technology brings the possibility of assisting the pathologist to predict the patients' survival from whole-slide pathological images (WSIs). However most of the prevalent methods only worked on the sampled patches in specifically or randomly selected tumor areas…