7 papers
Test Time Training for Supervised Causal Learning
Zizhen Deng, Jiaru Zhang, Rui Ding +5
Supervised Causal Learning (SCL) has shown promise in causal discovery by framing it as a supervised learning problem. However, it suffers from significant out-of-distribution gene…
OpenBreastUS: Benchmarking Neural Operators for Wave Imaging Using Breast Ultrasound Computed Tomography
Zhijun Zeng, Youjia Zheng, Hao Hu +8
Accurate and efficient simulation of wave equations is crucial in computational wave imaging applications, such as ultrasound computed tomography (USCT), which reconstructs tissue…
Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining
Qichen Sun, Zhengrui Guo, Rui Peng +2
Recent advances in computational pathology and artificial intelligence have significantly enhanced the utilization of gigapixel whole-slide images and and additional modalities (e.…
FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image Classification
Zhengrui Guo, Conghao Xiong, Jiabo Ma +4
Few-shot learning presents a critical solution for cancer diagnosis in computational pathology (CPath), addressing fundamental limitations in data availability, particularly the sc…
Neuron Platonic Intrinsic Representation From Dynamics Using Contrastive Learning
Wei Wu, Can Liao, Zizhen Deng +2
The Platonic Representation Hypothesis suggests a universal, modality-independent reality representation behind different data modalities. Inspired by this, we view each neuron as…
BLEND: Behavior-guided Neural Population Dynamics Modeling via Privileged Knowledge Distillation
Zhengrui Guo, Fangxu Zhou, Wei Wu +4
Modeling the nonlinear dynamics of neuronal populations represents a key pursuit in computational neuroscience. Recent research has increasingly focused on jointly modeling neural…