Liping Liu

Thomas Schnelldorfer, Janil Castro, Atoussa Goldar-Najafi et al.

For several cancer patients, operative resection with curative intent can end up in early recurrence of the cancer. Current limitations in peri-operative cancer staging and especially intra-operative misidentification of visible metastases is likely the main reason leading to unnecessary operative interventions in the affected individuals. Here, we evaluate whether an artificial intelligence (AI) system can improve recognition of peritoneal surface metastases on routine staging laparoscopy images from patients with gastrointestinal malignancies. In a simulated setting evaluating biopsied peritoneal lesions, a prototype deep learning surgical guidance system outperformed oncologic surgeons in identifying peritoneal surface metastases. In this environment the developed AI model would have improved the identification of metastases by 5% while reducing the number of unnecessary biopsies by 28% compared to current standard practice. Evaluating non-biopsied peritoneal lesions, the findings support the possibility that the AI system could identify peritoneal surface metastases that were falsely deemed benign in clinical practice. Our findings demonstrate the technical feasibility of an AI system for intra-operative identification of peritoneal surface metastases, but require future assessment in a multi-institutional clinical setting.

Hang Yin, Yao Su, Liping Liu et al.

Spike train classification has recently become an important topic in the machine learning community, where each spike train is a binary event sequence with \emph{temporal-sparsity of signals of interest} and \emph{temporal-noise} properties. A promising model for it should follow the design principle of performing intensive computation only when signals of interest appear. So such tasks use mainly Spiking Neural Networks (SNNs) due to their consideration of temporal-sparsity of spike trains. However, the basic mechanism of SNNs ignore the temporal-noise issue, which makes them computationally expensive and thus high power consumption for analyzing spike trains on resource-constrained platforms. As an event-driven model, an SNN neuron makes a reaction given any input signals, making it difficult to quickly find signals of interest. In this paper, we introduce an event-attention mechanism that enables SNNs to dynamically highlight useful signals of the original spike trains. To this end, we propose SkipSNN, which extends existing SNN models by learning to mask out noise by skipping membrane potential updates and shortening the effective size of the computational graph. This process is analogous to how people choose to open and close their eyes to filter the information they see. We evaluate SkipSNN on various neuromorphic tasks and demonstrate that it achieves significantly better computational efficiency and classification accuracy than other state-of-the-art SNNs.