11 citations · 25 across the 8 of their papers we have counts for
7 papers · 1 filter
Ensemble learning of pathology foundation models for precision oncology
Xiangde Luo, Xiyue Wang, Feyisope Eweje +24
Histopathology is essential for cancer diagnosis and treatment selection, and pathology foundation models learn visual representations from whole-slide images (WSIs). However, exis…
Effortless Cross-Platform Video Codec: A Codebook-Based Method
Kuan Tian, Yonghang Guan, Jinxi Xiang +3
Under certain circumstances, advanced neural video codecs can surpass the most complex traditional codecs in their rate-distortion (RD) performance. One of the main reasons for the…
Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder Approach
Feng Luo, Jinxi Xiang, Jun Zhang +2
The recent use of diffusion prior, enhanced by pre-trained text-image models, has markedly elevated the performance of image super-resolution (SR). To alleviate the huge computatio…
Towards Real-Time Neural Video Codec for Cross-Platform Application Using Calibration Information
Kuan Tian, Yonghang Guan, Jinxi Xiang +3
The state-of-the-art neural video codecs have outperformed the most sophisticated traditional codecs in terms of RD performance in certain cases. However, utilizing them for practi…
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…
Deep learning-based approach to reveal tumor mutational burden status from whole slide images across multiple cancer types
Siteng Chen, Jinxi Xiang, Xiyue Wang +6
Tumor mutational burden (TMB) is a potential genomic biomarker of immunotherapy. However, TMB detected through whole exome sequencing lacks clinical penetration in low-resource set…
Self-Ensembling Contrastive Learning for Semi-Supervised Medical Image Segmentation
Jinxi Xiang, Zhuowei Li, Wenji Wang +2
Deep learning has demonstrated significant improvements in medical image segmentation using a sufficiently large amount of training data with manual labels. Acquiring well-represen…