Search Results for author:
Found 10 papers, 5 papers with code
iMD4GC: Incomplete Multimodal Data Integration to Advance Precise Treatment Response Prediction and Survival Analysis for Gastric Cancer
The limited availability of modalities for each patient would cause information loss, adversely affecting predictive accuracy.
Histo-Genomic Knowledge Distillation For Cancer Prognosis From Histopathology Whole Slide Images
G-HANet is expected to be explored as a useful tool by the research community to address the current bottleneck of insufficient histo-genomic data pairing in the context of cancer prognosis and precision oncology.
HistGen: Histopathology Report Generation via Local-Global Feature Encoding and Cross-modal Context Interaction
Histopathology serves as the gold standard in cancer diagnosis, with clinical reports being vital in interpreting and understanding this process, guiding cancer treatment and patient care.
Prototypical Information Bottlenecking and Disentangling for Multimodal Cancer Survival Prediction
Despite advantages of multimodal learning for cancer survival prediction, massive redundancy in multimodal data prevents it from extracting discriminative and compact information: (1) An extensive amount of intra-modal task-unrelated information blurs discriminability, especially for gigapixel whole slide images (WSIs) with many patches in pathology and thousands of pathways in genomic data, leading to an ``intra-modal redundancy" issue.
Multimodal Optimal Transport-based Co-Attention Transformer with Global Structure Consistency for Survival Prediction
Survival prediction is a complicated ordinal regression task that aims to predict the ranking risk of death, which generally benefits from the integration of histology and genomic data.
Unsupervised Domain Adaptation via Deep Hierarchical Optimal Transport
In this paper, we propose a Deep Hierarchical Optimal Transport method (DeepHOT) for unsupervised domain adaptation.
Graph-based Visual-Semantic Entanglement Network for Zero-shot Image Recognition
Zero-shot learning uses semantic attributes to connect the search space of unseen objects.
Multiple Attentional Pyramid Networks for Chinese Herbal Recognition
Finally, a new framework for Chinese herbal recognition is proposed as a new application of APN.
Stochastic Region Pooling: Make Attention More Expressive
Global Average Pooling (GAP) is used by default on the channel-wise attention mechanism to extract channel descriptors.
Chinese Herbal Recognition based on Competitive Attentional Fusion of Multi-hierarchies Pyramid Features
According to the characteristics of herbal images, we proposed the competitive attentional fusion pyramid networks to model the features of herbal image, which mdoels the relationship of feature maps from different levels, and re-weights multi-level channels with channel-wise attention mechanism.
