M3T: Multi-Modal Medical Transformer to bridge Clinical Context with Visual Insights for Retinal Image Medical Description Generation

• URL: http://arxiv.org/abs/2406.13129v1
• Date: Wed, 19 Jun 2024 00:46:48 GMT
• Title: M3T: Multi-Modal Medical Transformer to bridge Clinical Context with Visual Insights for Retinal Image Medical Description Generation
• Authors: Nagur Shareef Shaik, Teja Krishna Cherukuri, Dong Hye Ye,
• Abstract summary: Multi-Modal Medical Transformer (M3T) is a novel deep learning architecture that integrates visual representations with diagnostic keywords. Experimental studies on the DeepEyeNet dataset validate the success of M3T in meeting ophthalmologists' standards.
• Score: 1.8789068567093286
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated retinal image medical description generation is crucial for streamlining medical diagnosis and treatment planning. Existing challenges include the reliance on learned retinal image representations, difficulties in handling multiple imaging modalities, and the lack of clinical context in visual representations. Addressing these issues, we propose the Multi-Modal Medical Transformer (M3T), a novel deep learning architecture that integrates visual representations with diagnostic keywords. Unlike previous studies focusing on specific aspects, our approach efficiently learns contextual information and semantics from both modalities, enabling the generation of precise and coherent medical descriptions for retinal images. Experimental studies on the DeepEyeNet dataset validate the success of M3T in meeting ophthalmologists' standards, demonstrating a substantial 13.5% improvement in BLEU@4 over the best-performing baseline model.

Related papers

• ZALM3: Zero-Shot Enhancement of Vision-Language Alignment via In-Context Information in Multi-Turn Multimodal Medical Dialogue [25.398370966763597]
  In our online medical consultation scenario, a doctor responds to the texts and images provided by a patient in multiple rounds to diagnose her/his health condition. Unlike high-quality images captured by professional equipment in traditional medical visual question answering (Med-VQA), the images in our case are taken by patients' mobile phones. We propose ZALM3, a Zero-shot strategy to improve vision-language alignment in Multi-turn Multimodal Medical dialogue.
  arXiv  Detail & Related papers  (2024-09-26T07:55:57Z)
• Autoregressive Sequence Modeling for 3D Medical Image Representation [48.706230961589924]
  We introduce a pioneering method for learning 3D medical image representations through an autoregressive sequence pre-training framework. Our approach various 3D medical images based on spatial, contrast, and semantic correlations, treating them as interconnected visual tokens within a token sequence.
  arXiv  Detail & Related papers  (2024-09-13T10:19:10Z)
• EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysis [20.318178211934985]
  We propose EyeCLIP, a visual-language foundation model developed using over 2.77 million ophthalmology images with partial text data. EyeCLIP can be transferred to a wide range of downstream tasks involving ocular and systemic diseases.
  arXiv  Detail & Related papers  (2024-09-10T17:00:19Z)
• QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge [93.61262892578067]
  Uncertainty in medical image segmentation tasks, especially inter-rater variability, presents a significant challenge. This variability directly impacts the development and evaluation of automated segmentation algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ)
  arXiv  Detail & Related papers  (2024-03-19T17:57:24Z)
• Eye-gaze Guided Multi-modal Alignment for Medical Representation Learning [65.54680361074882]
  Eye-gaze Guided Multi-modal Alignment (EGMA) framework harnesses eye-gaze data for better alignment of medical visual and textual features. We conduct downstream tasks of image classification and image-text retrieval on four medical datasets.
  arXiv  Detail & Related papers  (2024-03-19T03:59:14Z)
• Unified Medical Image Pre-training in Language-Guided Common Semantic Space [39.61770813855078]
  We propose an Unified Medical Image Pre-training framework, namely UniMedI. UniMedI uses diagnostic reports as common semantic space to create unified representations for diverse modalities of medical images. We evaluate its performance on both 2D and 3D images across 10 different datasets.
  arXiv  Detail & Related papers  (2023-11-24T22:01:12Z)
• Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training [51.16994853817024]
  This work focuses on designing an effective pre-training framework for 3D radiology images. We introduce Disruptive Autoencoders, a pre-training framework that attempts to reconstruct the original image from disruptions created by a combination of local masking and low-level perturbations. The proposed pre-training framework is tested across multiple downstream tasks and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-07-31T17:59:42Z)
• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)
• Multi-Modal Masked Autoencoders for Medical Vision-and-Language Pre-Training [62.215025958347105]
  We propose a self-supervised learning paradigm with multi-modal masked autoencoders. We learn cross-modal domain knowledge by reconstructing missing pixels and tokens from randomly masked images and texts.
  arXiv  Detail & Related papers  (2022-09-15T07:26:43Z)
• Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Training [5.119201893752376]
  We propose Medical Vision Language Learner (MedViLL) which adopts a Transformer-based architecture combined with a novel multimodal attention masking scheme. We empirically demonstrate the superior downstream task performance of MedViLL against various baselines including task-specific architectures.
  arXiv  Detail & Related papers  (2021-05-24T15:14:09Z)

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.