MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept Alignment

• URL: http://arxiv.org/abs/2401.08527v1
• Date: Tue, 16 Jan 2024 17:45:01 GMT
• Title: MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept Alignment
• Authors: Yequan Bie, Luyang Luo, Hao Chen
• Abstract summary: We propose a multi-modal explainable disease diagnosis framework that meticulously aligns medical images and clinical-related concepts semantically at multiple strata. Our method, while preserving model interpretability, attains high performance and label efficiency for concept detection and disease diagnosis.
• Score: 4.861768967055006
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Black-box deep learning approaches have showcased significant potential in the realm of medical image analysis. However, the stringent trustworthiness requirements intrinsic to the medical field have catalyzed research into the utilization of Explainable Artificial Intelligence (XAI), with a particular focus on concept-based methods. Existing concept-based methods predominantly apply concept annotations from a single perspective (e.g., global level), neglecting the nuanced semantic relationships between sub-regions and concepts embedded within medical images. This leads to underutilization of the valuable medical information and may cause models to fall short in harmoniously balancing interpretability and performance when employing inherently interpretable architectures such as Concept Bottlenecks. To mitigate these shortcomings, we propose a multi-modal explainable disease diagnosis framework that meticulously aligns medical images and clinical-related concepts semantically at multiple strata, encompassing the image level, token level, and concept level. Moreover, our method allows for model intervention and offers both textual and visual explanations in terms of human-interpretable concepts. Experimental results on three skin image datasets demonstrate that our method, while preserving model interpretability, attains high performance and label efficiency for concept detection and disease diagnosis.

Related papers

• 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)
• Aligning Human Knowledge with Visual Concepts Towards Explainable Medical Image Classification [8.382606243533942]
  We introduce a simple yet effective framework, Explicd, towards Explainable language-informed criteria-based diagnosis. By leveraging a pretrained vision-language model, Explicd injects these criteria into the embedding space as knowledge anchors. The final diagnostic outcome is determined based on the similarity scores between the encoded visual concepts and the textual criteria embeddings.
  arXiv  Detail & Related papers  (2024-06-08T23:23:28Z)
• MLIP: Enhancing Medical Visual Representation with Divergence Encoder and Knowledge-guided Contrastive Learning [48.97640824497327]
  We propose a novel framework leveraging domain-specific medical knowledge as guiding signals to integrate language information into the visual domain through image-text contrastive learning. Our model includes global contrastive learning with our designed divergence encoder, local token-knowledge-patch alignment contrastive learning, and knowledge-guided category-level contrastive learning with expert knowledge. Notably, MLIP surpasses state-of-the-art methods even with limited annotated data, highlighting the potential of multimodal pre-training in advancing medical representation learning.
  arXiv  Detail & Related papers  (2024-02-03T05:48:50Z)
• Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models [49.95603725998561]
  We propose a new paradigm to build robust and interpretable medical image classifiers with natural language concepts. Specifically, we first query clinical concepts from GPT-4, then transform latent image features into explicit concepts with a vision-language model.
  arXiv  Detail & Related papers  (2023-10-04T21:57:09Z)
• 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)
• Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images [52.527733226555206]
  We investigate the use of four attribution methods to explain a multiple instance learning models. We study two datasets of acute myeloid leukemia with over 100 000 single cell images. We compare attribution maps with the annotations of a medical expert to see how the model's decision-making differs from the human standard.
  arXiv  Detail & Related papers  (2023-03-15T14:00:11Z)
• ExAID: A Multimodal Explanation Framework for Computer-Aided Diagnosis of Skin Lesions [4.886872847478552]
  ExAID (Explainable AI for Dermatology) is a novel framework for biomedical image analysis. It provides multi-modal concept-based explanations consisting of easy-to-understand textual explanations. It will be the basis for similar applications in other biomedical imaging fields.
  arXiv  Detail & Related papers  (2022-01-04T17:11:28Z)
• 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)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)
• Learning Binary Semantic Embedding for Histology Image Classification and Retrieval [56.34863511025423]
  We propose a novel method for Learning Binary Semantic Embedding (LBSE) Based on the efficient and effective embedding, classification and retrieval are performed to provide interpretable computer-assisted diagnosis for histology images. Experiments conducted on three benchmark datasets validate the superiority of LBSE under various scenarios.
  arXiv  Detail & Related papers  (2020-10-07T08:36:44Z)

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.