6 papers
Calibrating Probabilistic Object Detectors with Annotator Disagreement
Zhi Qin Tan, Owen Addison, Yunpeng Li
High degrees of disagreement among annotators can exist for ambiguous objects, e.g. in medical images, underscoring the challenges of establishing ground truth annotations in objec…
Minimal Sufficient Representations for Self-interpretable Deep Neural Networks
Zhiyao Tan, Liu Li, Huazhen Lin
Deep neural networks (DNNs) achieve remarkable predictive performance but remain difficult to interpret, largely due to overparameterization that obscures the minimal structure req…
DentalX: Context-Aware Dental Disease Detection with Radiographs
Zhi Qin Tan, Xiatian Zhu, Owen Addison +1
Diagnosing dental diseases from radiographs is time-consuming and challenging due to the subtle nature of diagnostic evidence. Existing methods, which rely on object detection mode…
U-Mamba2: Scaling State Space Models for Dental Anatomy Segmentation in CBCT
Zhi Qin Tan, Xiatian Zhu, Owen Addison +1
Cone-Beam Computed Tomography (CBCT) is a widely used 3D imaging technique in dentistry, providing volumetric information about the anatomical structures of jaws and teeth. Accurat…
MICCAI STSR 2025 Challenge: Semi-Supervised Teeth and Pulp Segmentation and CBCT-IOS Registration
Yaqi Wang, Zhi Li, Chengyu Wu +15
Cone-Beam Computed Tomography (CBCT) and Intraoral Scanning (IOS) are essential for digital dentistry, but annotated data scarcity limits automated solutions for pulp canal segment…
U-Mamba2-SSL for Semi-Supervised Tooth and Pulp Segmentation in CBCT
Zhi Qin Tan, Xiatian Zhu, Owen Addison +1
Accurate segmentation of teeth and pulp in Cone-Beam Computed Tomography (CBCT) is vital for clinical applications like treatment planning and diagnosis. However, this process requ…