6 accepted papers
Nucleus detection and classification (NDC) in histopathology analysis is a fundamental task that underpins a wide range of high-level pathology applications. However, existing methods heavily rely on labor-intensive nucleus-level annotations and struggle to fully exploit large-scale unlabeled data f
Histopathology plays a critical role in medical diagnostics, with whole slide images (WSIs) offering valuable insights that directly influence clinical decision-making. However, the large size and complexity of WSIs may pose significant challenges for deep learning models, in both computational effi…
Motion planning is a difficult task, especially when generating feasible future trajectories in complex and interactive scenarios. While recent advancements in imitation-based planning have shown significant progress, this approach often encounters causal confusion in dynamic traffic environments. T
Modeling dynamics in the form of partial differential equations (PDEs) is an effectual way to understand real-world physics processes. For complex physics systems, analytical solutions are not available and numerical solutions are widely-used. However, traditional numerical algorithms are computatio…
Translating mental health recognition from clinical research into real-world application requires extensive data, yet existing emotion datasets are impoverished in terms of daily mental health monitoring, especially when aiming for self-reported anxiety and depression recognition. We introduce the J…
The cross-database context-dependent Text-to-SQL (XDTS) problem has attracted considerable attention in recent years due to its wide range of potential applications. However, we identify two biases in existing datasets for XDTS: (1) a high proportion of context-independent questions and (2) a high p…