20,360 research outputs found
MProtoNet: A Case-Based Interpretable Model for Brain Tumor Classification with 3D Multi-parametric Magnetic Resonance Imaging
Recent applications of deep convolutional neural networks in medical imaging
raise concerns about their interpretability. While most explainable deep
learning applications use post hoc methods (such as GradCAM) to generate
feature attribution maps, there is a new type of case-based reasoning models,
namely ProtoPNet and its variants, which identify prototypes during training
and compare input image patches with those prototypes. We propose the first
medical prototype network (MProtoNet) to extend ProtoPNet to brain tumor
classification with 3D multi-parametric magnetic resonance imaging (mpMRI)
data. To address different requirements between 2D natural images and 3D mpMRIs
especially in terms of localizing attention regions, a new attention module
with soft masking and online-CAM loss is introduced. Soft masking helps sharpen
attention maps, while online-CAM loss directly utilizes image-level labels when
training the attention module. MProtoNet achieves statistically significant
improvements in interpretability metrics of both correctness and localization
coherence (with a best activation precision of ) without
human-annotated labels during training, when compared with GradCAM and several
ProtoPNet variants. The source code is available at
https://github.com/aywi/mprotonet.Comment: 15 pages, 5 figures, 1 table; accepted for oral presentation at MIDL
2023 (https://openreview.net/forum?id=6Wbj3QCo4U4); camera-ready versio
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification
We present the Bayesian Case Model (BCM), a general framework for Bayesian
case-based reasoning (CBR) and prototype classification and clustering. BCM
brings the intuitive power of CBR to a Bayesian generative framework. The BCM
learns prototypes, the "quintessential" observations that best represent
clusters in a dataset, by performing joint inference on cluster labels,
prototypes and important features. Simultaneously, BCM pursues sparsity by
learning subspaces, the sets of features that play important roles in the
characterization of the prototypes. The prototype and subspace representation
provides quantitative benefits in interpretability while preserving
classification accuracy. Human subject experiments verify statistically
significant improvements to participants' understanding when using explanations
produced by BCM, compared to those given by prior art.Comment: Published in Neural Information Processing Systems (NIPS) 2014,
Neural Information Processing Systems (NIPS) 201
Neurocognitive Informatics Manifesto.
Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given
- …