Distilling BlackBox to Interpretable models for Efficient Transfer Learning

Building generalizable AI models is one of the primary challenges in the healthcare domain. While radiologists rely on generalizable descriptive rules of abnormality, Neural Network (NN) models suffer even with a slight shift in input distribution (\eg scanner type). Fine-tuning a model to transfer knowledge from one domain to another requires a significant amount of labeled data in the target domain. In this paper, we develop an interpretable model that can be efficiently fine-tuned to an unseen target domain with minimal computational cost. We assume the interpretable component of NN to be approximately domain-invariant. However, interpretable models typically underperform compared to their Blackbox (BB) variants. We start with a BB in the source domain and distill it into a \emph{mixture} of shallow interpretable models using human-understandable concepts. As each interpretable model covers a subset of data, a mixture of interpretable models achieves comparable performance as BB. Further, we use the pseudo-labeling technique from semi-supervised learning (SSL) to learn the concept classifier in the target domain, followed by fine-tuning the interpretable models in the target domain. We evaluate our model using a real-life large-scale chest-X-ray (CXR) classification dataset. The code is available at: \url{https://github.com/batmanlab/MICCAI-2023-Route-interpret-repeat-CXRs}.Comment: MICCAI, 2023, Early accep

SYSTEM AND METHOD FOR DETECTING INFINITE SIGNALING LOOP IN HIERARCHICAL NRF DEPLOYMENTS

For larger deployments, where multiple level of Network Function (NF) Repository Functions (NRFs) are deployed, the slightest misconfiguration could lead to an indefinite query loop between NRFs. The indefinite loop in the NRF query would mean repeated timeouts and/or failure of a service discovery request that a consumer had initiated to find a suitable producer. This could lead to signaling failure between a set of consumers and producers, which in turn can lead to Quality of Service (QoS) and Service Level Agreement (SLA) violations. A new Hypertext Transfer Protocol Version 2 (HTTP/2) header, Network Function (NF)-route-record, is proposed, which should be added by an intermediate NRF (relay or proxy) before forwarding any request. The NF-route-record should contain the identity of the peer from which the request was received. The receiving NRF checks the NF-route-record before further forwarding or serving the request. If the NF-route-record matches its own identity, then the receiving NRF would detect the loop. The same can be used in the reverse direction when sending the response from a server (producer) to a client (consumer)