INSightR-Net: Interpretable Neural Network for Regression using
Similarity-based Comparisons to Prototypical Examples
- URL: http://arxiv.org/abs/2208.00457v1
- Date: Sun, 31 Jul 2022 15:56:15 GMT
- Title: INSightR-Net: Interpretable Neural Network for Regression using
Similarity-based Comparisons to Prototypical Examples
- Authors: Linde S. Hesse and Ana I. L. Namburete
- Abstract summary: Convolutional neural networks (CNNs) have shown exceptional performance for a range of medical imaging tasks.
In this work, we propose an inherently interpretable CNN for regression using similarity-based comparisons.
A prototype layer incorporated into the architecture enables visualization of the areas in the image that are most similar to learned prototypes.
The final prediction is then intuitively modeled as a mean of prototype labels, weighted by the similarities.
- Score: 2.4366811507669124
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Convolutional neural networks (CNNs) have shown exceptional performance for a
range of medical imaging tasks. However, conventional CNNs are not able to
explain their reasoning process, therefore limiting their adoption in clinical
practice. In this work, we propose an inherently interpretable CNN for
regression using similarity-based comparisons (INSightR-Net) and demonstrate
our methods on the task of diabetic retinopathy grading. A prototype layer
incorporated into the architecture enables visualization of the areas in the
image that are most similar to learned prototypes. The final prediction is then
intuitively modeled as a mean of prototype labels, weighted by the
similarities. We achieved competitive prediction performance with our
INSightR-Net compared to a ResNet baseline, showing that it is not necessary to
compromise performance for interpretability. Furthermore, we quantified the
quality of our explanations using sparsity and diversity, two concepts
considered important for a good explanation, and demonstrated the effect of
several parameters on the latent space embeddings.
Related papers
- Characterization of topological structures in different neural network architectures [0.0]
We develop methods for analyzing representations from different architectures and check how one should use them to obtain valid results.
We applied these methods for ResNet, VGG19, and ViT architectures and found substantial differences along with some similarities.
arXiv Detail & Related papers (2024-07-08T18:02:18Z) - Graph Neural Networks for Learning Equivariant Representations of Neural Networks [55.04145324152541]
We propose to represent neural networks as computational graphs of parameters.
Our approach enables a single model to encode neural computational graphs with diverse architectures.
We showcase the effectiveness of our method on a wide range of tasks, including classification and editing of implicit neural representations.
arXiv Detail & Related papers (2024-03-18T18:01:01Z) - Multilayer Multiset Neuronal Networks -- MMNNs [55.2480439325792]
The present work describes multilayer multiset neuronal networks incorporating two or more layers of coincidence similarity neurons.
The work also explores the utilization of counter-prototype points, which are assigned to the image regions to be avoided.
arXiv Detail & Related papers (2023-08-28T12:55:13Z) - Revisiting Hidden Representations in Transfer Learning for Medical
Imaging [2.4545492329339815]
We compare ImageNet and RadImageNet on seven medical classification tasks.
Our results indicate that, contrary to intuition, ImageNet and RadImageNet may converge to distinct intermediate representations.
Our findings show that the similarity between networks before and after fine-tuning does not correlate with performance gains.
arXiv Detail & Related papers (2023-02-16T13:04:59Z) - Rethinking Semi-Supervised Medical Image Segmentation: A
Variance-Reduction Perspective [51.70661197256033]
We propose ARCO, a semi-supervised contrastive learning framework with stratified group theory for medical image segmentation.
We first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks.
We experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings.
arXiv Detail & Related papers (2023-02-03T13:50:25Z) - SPIN: An Empirical Evaluation on Sharing Parameters of Isotropic
Networks [25.465917853812538]
We present an empirical evaluation on methods for sharing parameters in isotropic networks.
We propose a weight sharing strategy to generate a family of models with better overall efficiency.
arXiv Detail & Related papers (2022-07-21T00:16:05Z) - Deep Co-Attention Network for Multi-View Subspace Learning [73.3450258002607]
We propose a deep co-attention network for multi-view subspace learning.
It aims to extract both the common information and the complementary information in an adversarial setting.
In particular, it uses a novel cross reconstruction loss and leverages the label information to guide the construction of the latent representation.
arXiv Detail & Related papers (2021-02-15T18:46:44Z) - Generative Counterfactuals for Neural Networks via Attribute-Informed
Perturbation [51.29486247405601]
We design a framework to generate counterfactuals for raw data instances with the proposed Attribute-Informed Perturbation (AIP)
By utilizing generative models conditioned with different attributes, counterfactuals with desired labels can be obtained effectively and efficiently.
Experimental results on real-world texts and images demonstrate the effectiveness, sample quality as well as efficiency of our designed framework.
arXiv Detail & Related papers (2021-01-18T08:37:13Z) - Toward Scalable and Unified Example-based Explanation and Outlier
Detection [128.23117182137418]
We argue for a broader adoption of prototype-based student networks capable of providing an example-based explanation for their prediction.
We show that our prototype-based networks beyond similarity kernels deliver meaningful explanations and promising outlier detection results without compromising classification accuracy.
arXiv Detail & Related papers (2020-11-11T05:58:17Z) - Investigation of REFINED CNN ensemble learning for anti-cancer drug
sensitivity prediction [0.0]
Anti-cancer drug sensitivity prediction using deep learning models for individual cell line is a significant challenge in personalized medicine.
REFINED CNN (Convolutional Neural Network) based models have shown promising results in drug sensitivity prediction.
We consider predictions based on ensembles built from such mappings that can improve upon the best single REFINED CNN model prediction.
arXiv Detail & Related papers (2020-09-09T02:27:29Z) - Similarity of Neural Networks with Gradients [8.804507286438781]
We propose to leverage both feature vectors and gradient ones into designing the representation of a neural network.
We show that the proposed approach provides a state-of-the-art method for computing similarity of neural networks.
arXiv Detail & Related papers (2020-03-25T17:04:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.