1 citations · 1 across the 5 of their papers we have counts for
5 papers
QuIIL at T3 challenge: Towards Automation in Life-Saving Intervention Procedures from First-Person View
Trinh T. L. Vuong, Doanh C. Bui, Jin Tae Kwak
In this paper, we present our solutions for a spectrum of automation tasks in life-saving intervention procedures within the Trauma THOMPSON (T3) Challenge, encompassing action rec…
FALFormer: Feature-aware Landmarks self-attention for Whole-slide Image Classification
Doanh C. Bui, Trinh Thi Le Vuong, Jin Tae Kwak
Slide-level classification for whole-slide images (WSIs) has been widely recognized as a crucial problem in digital and computational pathology. Current approaches commonly conside…
Towards a text-based quantitative and explainable histopathology image analysis
Anh Tien Nguyen, Trinh Thi Le Vuong, Jin Tae Kwak
Recently, vision-language pre-trained models have emerged in computational pathology. Previous works generally focused on the alignment of image-text pairs via the contrastive pre-…
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Simon Graham, Quoc Dang Vu, Mostafa Jahanifar +86
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovatio…
IMPaSh: A Novel Domain-shift Resistant Representation for Colorectal Cancer Tissue Classification
Trinh Thi Le Vuong, Quoc Dang Vu, Mostafa Jahanifar +3
The appearance of histopathology images depends on tissue type, staining and digitization procedure. These vary from source to source and are the potential causes for domain-shift…