97 research outputs found
Looking in the Right place for Anomalies: Explainable AI through Automatic Location Learning
Deep learning has now become the de facto approach to the recognition of
anomalies in medical imaging. Their 'black box' way of classifying medical
images into anomaly labels poses problems for their acceptance, particularly
with clinicians. Current explainable AI methods offer justifications through
visualizations such as heat maps but cannot guarantee that the network is
focusing on the relevant image region fully containing the anomaly. In this
paper, we develop an approach to explainable AI in which the anomaly is assured
to be overlapping the expected location when present. This is made possible by
automatically extracting location-specific labels from textual reports and
learning the association of expected locations to labels using a hybrid
combination of Bi-Directional Long Short-Term Memory Recurrent Neural Networks
(Bi-LSTM) and DenseNet-121. Use of this expected location to bias the
subsequent attention-guided inference network based on ResNet101 results in the
isolation of the anomaly at the expected location when present. The method is
evaluated on a large chest X-ray dataset.Comment: 5 pages, Paper presented as a poster at the International Symposium
on Biomedical Imaging, 2020, Paper Number 65
The Challenge of Unifying Semantic and Syntactic Inference Restrictions
International audienceWhile syntactic inference restrictions don't play an important role for SAT, they are an essential reasoning technique for more expressive logics, such as first-order logic, or fragments thereof. In particular, they can result in short proofs or model representations. On the other hand, semantically guided inference systems enjoy important properties, such as the generation of solely non-redundant clauses. I discuss to what extend the two paradigms may be unifiable
A knowledge base architecture for distributed knowledge agents
A tuple space based object oriented model for knowledge base representation and interpretation is presented. An architecture for managing distributed knowledge agents is then implemented within the model. The general model is based upon a database implementation of a tuple space. Objects are then defined as an additional layer upon the database. The tuple space may or may not be distributed depending upon the database implementation. A language for representing knowledge and inference strategy is defined whose implementation takes advantage of the tuple space. The general model may then be instantiated in many different forms, each of which may be a distinct knowledge agent. Knowledge agents may communicate using tuple space mechanisms as in the LINDA model as well as using more well known message passing mechanisms. An implementation of the model is presented describing strategies used to keep inference tractable without giving up expressivity. An example applied to a power management and distribution network for Space Station Freedom is given
- …