Evidential Concept Embedding Models: Towards Reliable Concept Explanations for Skin Disease Diagnosis

• URL: http://arxiv.org/abs/2406.19130v1
• Date: Thu, 27 Jun 2024 12:29:50 GMT
• Title: Evidential Concept Embedding Models: Towards Reliable Concept Explanations for Skin Disease Diagnosis
• Authors: Yibo Gao, Zheyao Gao, Xin Gao, Yuanye Liu, Bomin Wang, Xiahai Zhuang,
• Abstract summary: Concept Bottleneck Models (CBM) have emerged as an active interpretable framework incorporating human-interpretable concepts into decision-making. We propose an evidential Concept Embedding Model (evi-CEM) which employs evidential learning to model the concept uncertainty. Our evaluation demonstrates that evi-CEM achieves superior performance in terms of concept prediction.
• Score: 24.946148305384202
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Due to the high stakes in medical decision-making, there is a compelling demand for interpretable deep learning methods in medical image analysis. Concept Bottleneck Models (CBM) have emerged as an active interpretable framework incorporating human-interpretable concepts into decision-making. However, their concept predictions may lack reliability when applied to clinical diagnosis, impeding concept explanations' quality. To address this, we propose an evidential Concept Embedding Model (evi-CEM), which employs evidential learning to model the concept uncertainty. Additionally, we offer to leverage the concept uncertainty to rectify concept misalignments that arise when training CBMs using vision-language models without complete concept supervision. With the proposed methods, we can enhance concept explanations' reliability for both supervised and label-efficient settings. Furthermore, we introduce concept uncertainty for effective test-time intervention. Our evaluation demonstrates that evi-CEM achieves superior performance in terms of concept prediction, and the proposed concept rectification effectively mitigates concept misalignments for label-efficient training. Our code is available at https://github.com/obiyoag/evi-CEM.

Related papers

• How to Continually Adapt Text-to-Image Diffusion Models for Flexible Customization? [91.49559116493414]
  We propose a novel Concept-Incremental text-to-image Diffusion Model (CIDM) It can resolve catastrophic forgetting and concept neglect to learn new customization tasks in a concept-incremental manner. Experiments validate that our CIDM surpasses existing custom diffusion models.
  arXiv  Detail & Related papers  (2024-10-23T06:47:29Z)
• Concept Complement Bottleneck Model for Interpretable Medical Image Diagnosis [8.252227380729188]
  We propose a concept complement bottleneck model for interpretable medical image diagnosis. We propose to use concept adapters for specific concepts to mine the concept differences and score concepts in their own attention channels. Our model outperforms the state-of-the-art competitors in concept detection and disease diagnosis tasks.
  arXiv  Detail & Related papers  (2024-10-20T16:52:09Z)
• Discover-then-Name: Task-Agnostic Concept Bottlenecks via Automated Concept Discovery [52.498055901649025]
  Concept Bottleneck Models (CBMs) have been proposed to address the 'black-box' problem of deep neural networks. We propose a novel CBM approach -- called Discover-then-Name-CBM (DN-CBM) -- that inverts the typical paradigm. Our concept extraction strategy is efficient, since it is agnostic to the downstream task, and uses concepts already known to the model.
  arXiv  Detail & Related papers  (2024-07-19T17:50:11Z)
• Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck Models [57.86303579812877]
  Concept Bottleneck Models (CBMs) ground image classification on human-understandable concepts to allow for interpretable model decisions. Existing approaches often require numerous human interventions per image to achieve strong performances. We introduce a trainable concept realignment intervention module, which leverages concept relations to realign concept assignments post-intervention.
  arXiv  Detail & Related papers  (2024-05-02T17:59:01Z)
• Incremental Residual Concept Bottleneck Models [29.388549499546556]
  Concept Bottleneck Models (CBMs) map the black-box visual representations extracted by deep neural networks onto a set of interpretable concepts. We propose the Incremental Residual Concept Bottleneck Model (Res-CBM) to address the challenge of concept completeness. Our approach can be applied to any user-defined concept bank, as a post-hoc processing method to enhance the performance of any CBMs.
  arXiv  Detail & Related papers  (2024-04-13T12:02:19Z)
• On the Concept Trustworthiness in Concept Bottleneck Models [39.928868605678744]
  Concept Bottleneck Models (CBMs) break down the reasoning process into the input-to-concept mapping and the concept-to-label prediction. Despite the transparency of the concept-to-label prediction, the mapping from the input to the intermediate concept remains a black box. A pioneering metric, referred to as concept trustworthiness score, is proposed to gauge whether the concepts are derived from relevant regions. An enhanced CBM is introduced, enabling concept predictions to be made specifically from distinct parts of the feature map.
  arXiv  Detail & Related papers  (2024-03-21T12:24:53Z)
• Separable Multi-Concept Erasure from Diffusion Models [52.51972530398691]
  We propose a Separable Multi-concept Eraser (SepME) to eliminate unsafe concepts from large-scale diffusion models. The latter separates optimizable model weights, making each weight increment correspond to a specific concept erasure. Extensive experiments indicate the efficacy of our approach in eliminating concepts, preserving model performance, and offering flexibility in the erasure or recovery of various concepts.
  arXiv  Detail & Related papers  (2024-02-03T11:10:57Z)
• ConcEPT: Concept-Enhanced Pre-Training for Language Models [57.778895980999124]
  ConcEPT aims to infuse conceptual knowledge into pre-trained language models. It exploits external entity concept prediction to predict the concepts of entities mentioned in the pre-training contexts. Results of experiments show that ConcEPT gains improved conceptual knowledge with concept-enhanced pre-training.
  arXiv  Detail & Related papers  (2024-01-11T05:05:01Z)
• Coherent Concept-based Explanations in Medical Image and Its Application to Skin Lesion Diagnosis [0.0]
  Existing deep learning approaches for melanoma skin lesion diagnosis are deemed black-box models. We propose an inherently interpretable framework to improve the interpretability of concept-based models. Our method outperforms existing black-box and concept-based models for skin lesion classification.
  arXiv  Detail & Related papers  (2023-04-10T13:32:04Z)
• Concept Embedding Models [27.968589555078328]
  Concept bottleneck models promote trustworthiness by conditioning classification tasks on an intermediate level of human-like concepts. Existing concept bottleneck models are unable to find optimal compromises between high task accuracy, robust concept-based explanations, and effective interventions on concepts. We propose Concept Embedding Models, a novel family of concept bottleneck models which goes beyond the current accuracy-vs-interpretability trade-off by learning interpretable high-dimensional concept representations.
  arXiv  Detail & Related papers  (2022-09-19T14:49:36Z)
• Concept Gradient: Concept-based Interpretation Without Linear Assumption [77.96338722483226]
  Concept Activation Vector (CAV) relies on learning a linear relation between some latent representation of a given model and concepts. We proposed Concept Gradient (CG), extending concept-based interpretation beyond linear concept functions. We demonstrated CG outperforms CAV in both toy examples and real world datasets.
  arXiv  Detail & Related papers  (2022-08-31T17:06:46Z)

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.