In neurosurgery, accurate brain tissue characterization via probe-based Confocal Laser Endomicroscopy (pCLE) has become popular for guiding surgical decisions and ensuring safe tumour resections. In order to enable surgeons to trust a tissue classification model, interpretability of the result is required. However, state-of-the-art (SOTA) deep learning models for pCLE data classification exhibit limited interpretability. This paper introduces a novel image classification framework for interpretable brain tissue characterisation using pCLE data. Firstly, instead of the commonly employed cross-entropy based classification loss, we propose Label Contrastive Learning (LCL) loss to learn intra-category similarities and inter-category contrasts. We are then able to generate highly representative data embeddings, which not only improve classification performance but also distinguish characteristics from different tissue classes. Secondly, we design a Saliency Consistency (SC) module to enable the trained model to generate clinically relevant saliency maps of the input data. To further refine the saliency maps, a novel Top-K Maximum and Minimum Pooling (TK-MMP) layer is introduced to our SC module, to increase the contrast of saliency values between non-clinically relevant and clinically relevant areas. For the first time, the Exponential Moving Average (EMA) is used in a novel fashion to update global embeddings of the different tissue categories rather than the weights of the model. In addition, we propose a Global Embedding Inference (GEI) layer to replace learnable classification layers to achieve more robust classification by estimating the cosine similarity between the input data embeddings and global embeddings. Performance evaluation on ex-vivo and in-vivo pCLE brain data verifies that our proposed approach outperforms SOTA classification models in terms of accuracy, robustness and interpretability. Our source codes are released at: https://github.com/XC9292/LCL-SC.git.

Accurate instrument pose estimation is a crucial step towards the future of robotic surgery, enabling applications such as autonomous surgical task execution. Vision-based methods for surgical instrument pose estimation provide a practical approach to tool tracking, but they often require markers to be attached to the instruments. Recently, more research has focused on the development of markerless methods based on deep learning. However, acquiring realistic surgical data, with ground truth (GT) instrument poses, required for deep learning training, is challenging. To address the issues in surgical instrument pose estimation, we introduce the Surgical Robot Instrument Pose Estimation (SurgRIPE) challenge, hosted at the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2023. The objectives of this challenge are: (1) to provide the surgical vision community with realistic surgical video data paired with ground truth instrument poses, and (2) to establish a benchmark for evaluating markerless pose estimation methods. The challenge led to the development of several novel algorithms that showcased improved accuracy and robustness over existing methods. The performance evaluation study on the SurgRIPE dataset highlights the potential of these advanced algorithms to be integrated into robotic surgery systems, paving the way for more precise and autonomous surgical procedures. The SurgRIPE challenge has successfully established a new benchmark for the field, encouraging further research and development in surgical robot instrument pose estimation.