Aligning Human Knowledge with Visual Concepts Towards Explainable Medical Image Classification

• URL: http://arxiv.org/abs/2406.05596v1
• Date: Sat, 8 Jun 2024 23:23:28 GMT
• Title: Aligning Human Knowledge with Visual Concepts Towards Explainable Medical Image Classification
• Authors: Yunhe Gao, Difei Gu, Mu Zhou, Dimitris Metaxas,
• Abstract summary: 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.
• Score: 8.382606243533942
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Although explainability is essential in the clinical diagnosis, most deep learning models still function as black boxes without elucidating their decision-making process. In this study, we investigate the explainable model development that can mimic the decision-making process of human experts by fusing the domain knowledge of explicit diagnostic criteria. We introduce a simple yet effective framework, Explicd, towards Explainable language-informed criteria-based diagnosis. Explicd initiates its process by querying domain knowledge from either large language models (LLMs) or human experts to establish diagnostic criteria across various concept axes (e.g., color, shape, texture, or specific patterns of diseases). By leveraging a pretrained vision-language model, Explicd injects these criteria into the embedding space as knowledge anchors, thereby facilitating the learning of corresponding visual concepts within medical images. The final diagnostic outcome is determined based on the similarity scores between the encoded visual concepts and the textual criteria embeddings. Through extensive evaluation of five medical image classification benchmarks, Explicd has demonstrated its inherent explainability and extends to improve classification performance compared to traditional black-box models.

Related papers

• Aligning Characteristic Descriptors with Images for Human-Expert-like Explainability [0.0]
  In mission-critical domains such as law enforcement and medical diagnosis, the ability to explain and interpret the outputs of deep learning models is crucial. We propose a novel approach that utilizes characteristic descriptors to explain model decisions by identifying their presence in images.
  arXiv  Detail & Related papers  (2024-11-06T15:47:18Z)
• A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysis [48.84443450990355]
  Deep networks have achieved broad success in analyzing natural images, when applied to medical scans, they often fail in unexcepted situations. We investigate this challenge and focus on model sensitivity to domain shifts, such as data sampled from different hospitals or data confounded by demographic variables such as sex, race, etc, in the context of chest X-rays and skin lesion images. Taking inspiration from medical training, we propose giving deep networks a prior grounded in explicit medical knowledge communicated in natural language.
  arXiv  Detail & Related papers  (2024-05-23T17:55:02Z)
• MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept Alignment [4.861768967055006]
  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.
  arXiv  Detail & Related papers  (2024-01-16T17:45:01Z)
• Multi-modal vision-language model for generalizable annotation-free pathology localization and clinical diagnosis [18.958171123895866]
  Defining pathologies automatically from medical images aids the understanding of the emergence and progression of diseases. Existing deep learning models heavily rely on expert annotations and lack generalization capabilities in open clinical environments. We present a vision-language model for. lingering-free pathology (AFLoc) We demonstrate that AFLoc surpasses state-of-the-art methods in pathology and classification, and even outperforms the human benchmark in locating 5 different pathologies.
  arXiv  Detail & Related papers  (2024-01-04T03:09:39Z)
• Towards Concept-based Interpretability of Skin Lesion Diagnosis using Vision-Language Models [0.0]
  We show that vision-language models can be used to alleviate the dependence on a large number of concept-annotated samples. In particular, we propose an embedding learning strategy to adapt CLIP to the downstream task of skin lesion classification using concept-based descriptions as textual embeddings.
  arXiv  Detail & Related papers  (2023-11-24T08:31:34Z)
• 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)
• 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)
• Explainable Deep Learning Methods in Medical Image Classification: A Survey [0.0]
  State-of-the-art deep learning models have achieved human-level accuracy on the classification of different types of medical data. These models are hardly adopted in clinical, mainly due to their lack of interpretability. The black-box-ness of deep learning models has raised the need for devising strategies to explain the decision process of these models.
  arXiv  Detail & Related papers  (2022-05-10T09:28:14Z)
• Interpretable Vertebral Fracture Diagnosis [69.68641439851777]
  Black-box neural network models learn clinically relevant features for fracture diagnosis. This work identifies the concepts networks use for vertebral fracture diagnosis in CT images.
  arXiv  Detail & Related papers  (2022-03-30T13:07:41Z)
• BI-RADS-Net: An Explainable Multitask Learning Approach for Cancer Diagnosis in Breast Ultrasound Images [69.41441138140895]
  This paper introduces BI-RADS-Net, a novel explainable deep learning approach for cancer detection in breast ultrasound images. The proposed approach incorporates tasks for explaining and classifying breast tumors, by learning feature representations relevant to clinical diagnosis. Explanations of the predictions (benign or malignant) are provided in terms of morphological features that are used by clinicians for diagnosis and reporting in medical practice.
  arXiv  Detail & Related papers  (2021-10-05T19:14:46Z)
• 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)

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.