AI-Driven Radiology Report Generation for Traumatic Brain Injuries

• URL: http://arxiv.org/abs/2510.08498v1
• Date: Thu, 09 Oct 2025 17:39:04 GMT
• Title: AI-Driven Radiology Report Generation for Traumatic Brain Injuries
• Abstract summary: We propose an AI-based approach for automatic radiology report generation tailored to cranial trauma cases.<n>Our model integrates an AC-BiFPN with a Transformer architecture to capture and process complex medical imaging data.<n>We evaluate the performance of our model on the RSNA Intracranial Hemorrhage Detection dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Traumatic brain injuries present significant diagnostic challenges in emergency medicine, where the timely interpretation of medical images is crucial for patient outcomes. In this paper, we propose a novel AI-based approach for automatic radiology report generation tailored to cranial trauma cases. Our model integrates an AC-BiFPN with a Transformer architecture to capture and process complex medical imaging data such as CT and MRI scans. The AC-BiFPN extracts multi-scale features, enabling the detection of intricate anomalies like intracranial hemorrhages, while the Transformer generates coherent, contextually relevant diagnostic reports by modeling long-range dependencies. We evaluate the performance of our model on the RSNA Intracranial Hemorrhage Detection dataset, where it outperforms traditional CNN-based models in both diagnostic accuracy and report generation. This solution not only supports radiologists in high-pressure environments but also provides a powerful educational tool for trainee physicians, offering real-time feedback and enhancing their learning experience. Our findings demonstrate the potential of combining advanced feature extraction with transformer-based text generation to improve clinical decision-making in the diagnosis of traumatic brain injuries.

Related papers

• AI-Enhanced Virtual Biopsies for Brain Tumor Diagnosis in Low Resource Settings [0.0]
  This paper presents a prototype virtual biopsy pipeline for four-class classification of 2D brain MRI images using a lightweight convolutional neural network (CNN) and radiomics-style handcrafted features.<n>The system is framed as decision support and not a substitute for clinical diagnosis or histopathology.
  arXiv  Detail & Related papers  (2025-12-19T19:53:56Z)
• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• Brain Stroke Detection and Classification Using CT Imaging with Transformer Models and Explainable AI [0.0]
  This study proposed an artificial intelligence framework for multiclass stroke classification using CT scan images.<n>The proposed method adopted MaxViT, a state-of-the-art Vision Transformer, as the primary deep learning model for image-based stroke classification.<n>To enhance model generalization and address class imbalance, we applied data augmentation techniques.
  arXiv  Detail & Related papers  (2025-07-13T13:50:50Z)
• Causal Disentanglement for Robust Long-tail Medical Image Generation [80.15257897500578]
  We propose a novel medical image generation framework, which generates independent pathological and structural features.<n>We leverage a diffusion model guided by pathological findings to model pathological features, enabling the generation of diverse counterfactual images.
  arXiv  Detail & Related papers  (2025-04-20T01:54:18Z)
• Comparative Evaluation of Radiomics and Deep Learning Models for Disease Detection in Chest Radiography [0.0]
  We evaluate radiomics-based and deep learning-based approaches for disease detection in chest radiography.<n>Deep learning models learn directly from image data, while radiomics-based models extract handcrafted features.<n>These findings provide statistically validated, data-driven recommendations for model selection in diagnostic AI.
  arXiv  Detail & Related papers  (2025-04-16T16:54:37Z)
• Abn-BLIP: Abnormality-aligned Bootstrapping Language-Image Pre-training for Pulmonary Embolism Diagnosis and Report Generation from CTPA [6.257940655794537]
  Abn-BLIP is an advanced diagnosis model designed to align abnormal findings to generate the accuracy and comprehensiveness of radiology reports.<n>Our experiments show that Abn-BLIP outperforms state-of-the-art medical vision-language models and 3D report generation methods in both accuracy and clinical relevance.
  arXiv  Detail & Related papers  (2025-03-03T20:13:39Z)
• AutoRG-Brain: Grounded Report Generation for Brain MRI [57.22149878985624]
  Radiologists are tasked with interpreting a large number of images in a daily base, with the responsibility of generating corresponding reports. This demanding workload elevates the risk of human error, potentially leading to treatment delays, increased healthcare costs, revenue loss, and operational inefficiencies. We initiate a series of work on grounded Automatic Report Generation (AutoRG) This system supports the delineation of brain structures, the localization of anomalies, and the generation of well-organized findings.
  arXiv  Detail & Related papers  (2024-07-23T17:50:00Z)
• Summarizing Radiology Reports Findings into Impressions [1.8964110318127383]
  We present a model with state-of-art radiology report summarization performance. We also provide an analysis of the model limitations and radiology knowledge gain. Our best performing model was a fine-tuned BERT-to-BERT encoder-decoder with 58.75/100 ROUGE-L F1.
  arXiv  Detail & Related papers  (2024-05-10T20:29:25Z)
• 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)
• Cross-Modal Causal Intervention for Medical Report Generation [107.76649943399168]
  Radiology Report Generation (RRG) is essential for computer-aided diagnosis and medication guidance.<n> generating accurate lesion descriptions remains challenging due to spurious correlations from visual-linguistic biases.<n>We propose a two-stage framework named CrossModal Causal Representation Learning (CMCRL)<n> Experiments on IU-Xray and MIMIC-CXR show that our CMCRL pipeline significantly outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-03-16T07:23:55Z)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• Hierarchical Analysis of Visual COVID-19 Features from Chest Radiographs [5.832030105874915]
  We model radiological features with a human-interpretable class hierarchy that aligns with the radiological decision process. Experiments show that model failures highly correlate with ICU imaging conditions and with the inherent difficulty in distinguishing certain types of radiological features.
  arXiv  Detail & Related papers  (2021-07-14T11:37:28Z)

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.