DWARF: Disease-weighted network for attention map refinement

• URL: http://arxiv.org/abs/2406.17032v2
• Date: Fri, 28 Jun 2024 14:34:00 GMT
• Title: DWARF: Disease-weighted network for attention map refinement
• Authors: Haozhe Luo, Aurélie Pahud de Mortanges, Oana Inel, Abraham Bernstein, Mauricio Reyes,
• Abstract summary: This study addresses the "human out of the loop" and "trustworthiness" issues in medical image analysis by integrating medical professionals into the interpretability process. We propose a disease-weighted attention map refinement network (DWARF) that leverages expert feedback to enhance model relevance and accuracy.
• Score: 6.658039889530641
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The interpretability of deep learning is crucial for evaluating the reliability of medical imaging models and reducing the risks of inaccurate patient recommendations. This study addresses the "human out of the loop" and "trustworthiness" issues in medical image analysis by integrating medical professionals into the interpretability process. We propose a disease-weighted attention map refinement network (DWARF) that leverages expert feedback to enhance model relevance and accuracy. Our method employs cyclic training to iteratively improve diagnostic performance, generating precise and interpretable feature maps. Experimental results demonstrate significant improvements in interpretability and diagnostic accuracy across multiple medical imaging datasets. This approach fosters effective collaboration between AI systems and healthcare professionals, ultimately aiming to improve patient outcomes

Related papers

• Analyzing the Effect of $k$-Space Features in MRI Classification Models [0.0]
  We have developed an explainable AI methodology tailored for medical imaging. We employ a Convolutional Neural Network (CNN) that analyzes MRI scans across both image and frequency domains. This approach not only enhances early training efficiency but also deepens our understanding of how additional features impact the model predictions.
  arXiv  Detail & Related papers  (2024-09-20T15:43:26Z)
• Distributed Federated Learning-Based Deep Learning Model for Privacy MRI Brain Tumor Detection [11.980634373191542]
  Distributed training can facilitate the processing of large medical image datasets, and improve the accuracy and efficiency of disease diagnosis. This paper presents an innovative approach to medical image classification, leveraging Federated Learning (FL) to address the dual challenges of data privacy and efficient disease diagnosis.
  arXiv  Detail & Related papers  (2024-04-15T09:07:19Z)
• Hypergraph Convolutional Networks for Fine-grained ICU Patient Similarity Analysis and Risk Prediction [15.06049250330114]
  The Intensive Care Unit (ICU) is one of the most important parts of a hospital, which admits critically ill patients and provides continuous monitoring and treatment. Various patient outcome prediction methods have been attempted to assist healthcare professionals in clinical decision-making.
  arXiv  Detail & Related papers  (2023-08-24T05:26:56Z)
• Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges [58.32937972322058]
  "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image (MedAI 2021)" competitions. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic.
  arXiv  Detail & Related papers  (2023-07-30T16:08:45Z)
• SHAMSUL: Systematic Holistic Analysis to investigate Medical Significance Utilizing Local interpretability methods in deep learning for chest radiography pathology prediction [1.0138723409205497]
  The study delves into the application of four well-established interpretability methods: Local Interpretable Model-agnostic Explanations (LIME), Shapley Additive exPlanations (SHAP), Gradient-weighted Class Activation Mapping (Grad-CAM) and Layer-wise Relevance Propagation (LRP) Our analysis encompasses both single-label and multi-label predictions, providing a comprehensive and unbiased assessment through quantitative and qualitative investigations, which are compared against human expert annotation.
  arXiv  Detail & Related papers  (2023-07-16T11:10:35Z)
• A Trustworthy Framework for Medical Image Analysis with Deep Learning [71.48204494889505]
  TRUDLMIA is a trustworthy deep learning framework for medical image analysis. It is anticipated that the framework will support researchers and clinicians in advancing the use of deep learning for dealing with public health crises including COVID-19.
  arXiv  Detail & Related papers  (2022-12-06T05:30:22Z)
• Towards Trustworthy Automatic Diagnosis Systems by Emulating Doctors' Reasoning with Deep Reinforcement Learning [2.314562406457073]
  We propose to model the evidence acquisition and automatic diagnosis tasks using a deep reinforcement learning framework. We show that our approach performs better than existing models while maintaining competitive pathology prediction accuracy.
  arXiv  Detail & Related papers  (2022-10-13T17:17:17Z)
• Robust and Efficient Medical Imaging with Self-Supervision [80.62711706785834]
  We present REMEDIS, a unified representation learning strategy to improve robustness and data-efficiency of medical imaging AI. We study a diverse range of medical imaging tasks and simulate three realistic application scenarios using retrospective data.
  arXiv  Detail & Related papers  (2022-05-19T17:34:18Z)
• Predicting Patient Readmission Risk from Medical Text via Knowledge Graph Enhanced Multiview Graph Convolution [67.72545656557858]
  We propose a new method that uses medical text of Electronic Health Records for prediction. We represent discharge summaries of patients with multiview graphs enhanced by an external knowledge graph. Experimental results prove the effectiveness of our method, yielding state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-12-19T01:45:57Z)
• 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)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)

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.