Ranga Rodrigo

These works advance AI-driven cancer pathology by addressing two key challenges: multimodal data integration and limited annotations. The SENCA-st framework introduces a cross-attention-based shared encoder to effectively fuse histopathology images with spatial transcriptomics, enabling precise identification of tumor heterogeneity and microenvironmental regions by capturing both structural and functional differences. Complementing this, the uncertainty-aware learning approach improves cancer subtyping by estimating model confidence and strategically selecting only the most informative samples for expert annotation, achieving state-of-the-art performance with minimal labeled data. Together, these contributions provide a scalable and efficient pathway for more accurate and data-efficient computational pathology systems.

Point clouds, usually obtained using LiDARs, is an important perception manner commonplace in vision based autonomous navigation. One challenge of large-scale outdoor LiDAR point clouds is the high volume of points; generally millions of points per frame of observation. Object detection, and semantic segmentation important problems in the point-cloud domain.

Computer vision is an enabler for self-driving, whether the input is camera based-video or LIDAR-based point clouds. We are working along multiple avenues to contribute to this important area. Road marking detection and lane detection directly assist self-driving. We have introduced a novel road marking benchmark dataset for road marking detection, addressing the limitations in the existing publicly available datasets such as lack of challenging scenarios, prominence given to lane markings, unavailability of an evaluation script, lack of annotation formats and lower resolutions. In SwiftLane, we presented a simple and light-weight, end-to-end deep learning based framework, coupled with the row-wise classification formulation for fast and efficient lane detection. Both these were with the support from Creative Software under the supervision of Dr. Peshala Jayasekara. Inspired by recent improvements in point cloud processing for autonomous navigation, we focused on using hierarchical graph neural networks for processing and feature learning over large-scale outdoor LiDAR point clouds. In point based and GNN models for semantic segmentation with our work achieves a significant improvement for GNNs on the SemanticKITTI dataset. We have also made a more fundamental contribution in using cross-modal contrastive learning approach to learn transferable 3D point cloud representations.