VALD-MD: Visual Attribution via Latent Diffusion for Medical Diagnostics

• URL: http://arxiv.org/abs/2401.01414v1
• Date: Tue, 2 Jan 2024 19:51:49 GMT
• Title: VALD-MD: Visual Attribution via Latent Diffusion for Medical Diagnostics
• Authors: Ammar A. Siddiqui (1), Santosh Tirunagari (1), Tehseen Zia (2), David Windridge (1) ((1) Middlesex University, London, UK, (2) COMSATS University, Islamabad, Pakistan)
• Abstract summary: Visual attribution in medical imaging seeks to make evident the diagnostically-relevant components of a medical image. We here present a novel generative visual attribution technique, one that leverages latent diffusion models in combination with domain-specific large language models. The resulting system also exhibits a range of latent capabilities including zero-shot localized disease induction.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual attribution in medical imaging seeks to make evident the diagnostically-relevant components of a medical image, in contrast to the more common detection of diseased tissue deployed in standard machine vision pipelines (which are less straightforwardly interpretable/explainable to clinicians). We here present a novel generative visual attribution technique, one that leverages latent diffusion models in combination with domain-specific large language models, in order to generate normal counterparts of abnormal images. The discrepancy between the two hence gives rise to a mapping indicating the diagnostically-relevant image components. To achieve this, we deploy image priors in conjunction with appropriate conditioning mechanisms in order to control the image generative process, including natural language text prompts acquired from medical science and applied radiology. We perform experiments and quantitatively evaluate our results on the COVID-19 Radiography Database containing labelled chest X-rays with differing pathologies via the Frechet Inception Distance (FID), Structural Similarity (SSIM) and Multi Scale Structural Similarity Metric (MS-SSIM) metrics obtained between real and generated images. The resulting system also exhibits a range of latent capabilities including zero-shot localized disease induction, which are evaluated with real examples from the cheXpert dataset.

Related papers

• Discrepancy-based Diffusion Models for Lesion Detection in Brain MRI [1.8420387715849447]
  Diffusion probabilistic models (DPMs) have exhibited significant effectiveness in computer vision tasks. Their notable performance heavily relies on labelled datasets, which limits their application in medical images. This paper introduces a novel framework by incorporating distinctive discrepancy features.
  arXiv  Detail & Related papers  (2024-05-08T11:26:49Z)
• FeaInfNet: Diagnosis in Medical Image with Feature-Driven Inference and Visual Explanations [4.022446255159328]
  Interpretable deep learning models have received widespread attention in the field of image recognition. Many interpretability models that have been proposed still have problems of insufficient accuracy and interpretability in medical image disease diagnosis. We propose feature-driven inference network (FeaInfNet) to solve these problems.
  arXiv  Detail & Related papers  (2023-12-04T13:09:00Z)
• Radiology Report Generation Using Transformers Conditioned with Non-imaging Data [55.17268696112258]
  This paper proposes a novel multi-modal transformer network that integrates chest x-ray (CXR) images and associated patient demographic information. The proposed network uses a convolutional neural network to extract visual features from CXRs and a transformer-based encoder-decoder network that combines the visual features with semantic text embeddings of patient demographic information.
  arXiv  Detail & Related papers  (2023-11-18T14:52:26Z)
• Beyond Images: An Integrative Multi-modal Approach to Chest X-Ray Report Generation [47.250147322130545]
  Image-to-text radiology report generation aims to automatically produce radiology reports that describe the findings in medical images. Most existing methods focus solely on the image data, disregarding the other patient information accessible to radiologists. We present a novel multi-modal deep neural network framework for generating chest X-rays reports by integrating structured patient data, such as vital signs and symptoms, alongside unstructured clinical notes.
  arXiv  Detail & Related papers  (2023-11-18T14:37:53Z)
• Introducing Shape Prior Module in Diffusion Model for Medical Image Segmentation [7.7545714516743045]
  We propose an end-to-end framework called VerseDiff-UNet, which leverages the denoising diffusion probabilistic model (DDPM) Our approach integrates the diffusion model into a standard U-shaped architecture. We evaluate our method on a single dataset of spine images acquired through X-ray imaging.
  arXiv  Detail & Related papers  (2023-09-12T03:05:00Z)
• Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains [3.8137985834223502]
  Building generative models for medical images that faithfully depict clinical context may help alleviate the paucity of healthcare datasets. We explore the sub-components of the Stable Diffusion pipeline to fine-tune the model to generate medical images. Our best-performing model improves upon the stable diffusion baseline and can be conditioned to insert a realistic-looking abnormality on a synthetic radiology image.
  arXiv  Detail & Related papers  (2022-10-09T01:43:08Z)
• Medical Image Captioning via Generative Pretrained Transformers [57.308920993032274]
  We combine two language models, the Show-Attend-Tell and the GPT-3, to generate comprehensive and descriptive radiology records. The proposed model is tested on two medical datasets, the Open-I, MIMIC-CXR, and the general-purpose MS-COCO.
  arXiv  Detail & Related papers  (2022-09-28T10:27:10Z)
• Generative Residual Attention Network for Disease Detection [51.60842580044539]
  We present a novel approach for disease generation in X-rays using a conditional generative adversarial learning. We generate a corresponding radiology image in a target domain while preserving the identity of the patient. We then use the generated X-ray image in the target domain to augment our training to improve the detection performance.
  arXiv  Detail & Related papers  (2021-10-25T14:15:57Z)
• Cross-Modal Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop [63.81818077092879]
  We propose an end-to-end semi-supervised cross-modal contrastive learning framework for medical images. We first apply an image encoder to classify the chest X-rays and to generate the image features. The radiomic features are then passed through another dedicated encoder to act as the positive sample for the image features generated from the same chest X-ray.
  arXiv  Detail & Related papers  (2021-04-11T09:16:29Z)
• Malignancy Prediction and Lesion Identification from Clinical Dermatological Images [65.1629311281062]
  We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images. We first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy.
  arXiv  Detail & Related papers  (2021-04-02T20:52:05Z)

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.