28,125 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
Enhancing Creativity in Interaction Design: Alternative Design Brief
This paper offers a critique of the design brief as it is currently used in teaching interaction design and proposes an alternative way of developing it. Such a design brief requires the exploration of alternative application domains for an already developed technology. The paper presents a case study where such a novel type of design brief has been offered to the students taking part in a collaborative design project and discusses how it supported divergent thinking and creativity as well as helped enhancing the learning objectives
Business models for sustained ehealth implementation: lessons from two continents
There is general consensus that Computers and Information Technology have the potential to enhance health systems applications, and many good examples of such applications exist all over the world. Unfortunately, with respect to eHealth and telemedicine, there is much disillusionment and scepticism. This paper describes two models that were developed separately, but had the same purpose, namely to facilitate a holistic approach to the development and implementation of eHealth solutions. The roadmap of the Centre for eHealth Research (CeHRes roadmap) was developed in the Netherlands, and the Telemedicine Maturity Model (TMMM) was developed in South Africa. The purpose of this paper is to analyse the commonalities and differences of these approaches, and to explore how they can be used to complement each other. The first part of this paper comprises of a comparison of these models in terms of origin, research domain and design principles. Case comparisons are then presented to illustrate how these models complement one another
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
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