3 research outputs found
Deep Learning for Distinguishing Normal versus Abnormal Chest Radiographs and Generalization to Unseen Diseases
Chest radiography (CXR) is the most widely-used thoracic clinical imaging
modality and is crucial for guiding the management of cardiothoracic
conditions. The detection of specific CXR findings has been the main focus of
several artificial intelligence (AI) systems. However, the wide range of
possible CXR abnormalities makes it impractical to build specific systems to
detect every possible condition. In this work, we developed and evaluated an AI
system to classify CXRs as normal or abnormal. For development, we used a
de-identified dataset of 248,445 patients from a multi-city hospital network in
India. To assess generalizability, we evaluated our system using 6
international datasets from India, China, and the United States. Of these
datasets, 4 focused on diseases that the AI was not trained to detect: 2
datasets with tuberculosis and 2 datasets with coronavirus disease 2019. Our
results suggest that the AI system generalizes to new patient populations and
abnormalities. In a simulated workflow where the AI system prioritized abnormal
cases, the turnaround time for abnormal cases reduced by 7-28%. These results
represent an important step towards evaluating whether AI can be safely used to
flag cases in a general setting where previously unseen abnormalities exist
ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders
Our approach, which we call Embeddings for Language/Image-aligned X-Rays, or
ELIXR, leverages a language-aligned image encoder combined or grafted onto a
fixed LLM, PaLM 2, to perform a broad range of tasks. We train this lightweight
adapter architecture using images paired with corresponding free-text radiology
reports from the MIMIC-CXR dataset. ELIXR achieved state-of-the-art performance
on zero-shot chest X-ray (CXR) classification (mean AUC of 0.850 across 13
findings), data-efficient CXR classification (mean AUCs of 0.893 and 0.898
across five findings (atelectasis, cardiomegaly, consolidation, pleural
effusion, and pulmonary edema) for 1% (~2,200 images) and 10% (~22,000 images)
training data), and semantic search (0.76 normalized discounted cumulative gain
(NDCG) across nineteen queries, including perfect retrieval on twelve of them).
Compared to existing data-efficient methods including supervised contrastive
learning (SupCon), ELIXR required two orders of magnitude less data to reach
similar performance. ELIXR also showed promise on CXR vision-language tasks,
demonstrating overall accuracies of 58.7% and 62.5% on visual question
answering and report quality assurance tasks, respectively. These results
suggest that ELIXR is a robust and versatile approach to CXR AI
Intelligent Ultrasound Imaging for Enhanced Breast Cancer Diagnosis: Ensemble Transfer Learning Strategies
According to WHO statistics for 2018, there are 1.2 million cases and 700,000 deaths from breast cancer (BC) each year, making it the second-highest cause of mortality for women globally. In recent years, advances in artificial (AI) intelligence and machine (ML) learning have shown incredible potential in increasing the accuracy and efficiency of BC diagnosis. This research describes an intelligent BC image analysis system that leverages the capabilities of transfer learning (TLs) with ensemble stacking ML models. As part of this research, we created a model for analyzing ultrasound BC images using cutting-edge TL models such as Inception V3, VGG-19, and VGG-16. We have implemented stacking of ensemble ML models, including MLP (Multi-Layer Perceptron) with different architectures (10 10, 20 20, and 30 30) and Support Vector Machines (SVM) with RBF and Polynomial kernels. We analyzed the effectiveness of the proposed system in performance parameters (accuracy (CA), sensitivity, specificity, and AUC). Compared to the results with existing BC diagnostic systems, the proposed method (Inception V3 + Staking) is superior, with performance parameters 0.947 of AUC and 0.858 of CA values. The proposed BCUI analysis system consists of data collection, pre-processing, transfer learning, ensemble stacking of ML models, and performance evaluation, with comparative analysis demonstrating its superiority over existing methods