AIMC Topic: Radiographic Image Interpretation, Computer-Assisted

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A New Benchmark: Clinical Uncertainty and Severity Aware Labeled Chest X-Ray Images With Multi-Relationship Graph Learning.

IEEE transactions on medical imaging
Chest radiography, commonly known as CXR, is frequently utilized in clinical settings to detect cardiopulmonary conditions. However, even seasoned radiologists might offer different evaluations regarding the seriousness and uncertainty associated wit...

Weakly Supervised Object Detection in Chest X-Rays With Differentiable ROI Proposal Networks and Soft ROI Pooling.

IEEE transactions on medical imaging
Weakly supervised object detection (WSup-OD) increases the usefulness and interpretability of image classification algorithms without requiring additional supervision. The successes of multiple instance learning in this task for natural images, howev...

Implementing an AI algorithm in the clinical setting: a case study for the accuracy paradox.

European radiology
OBJECTIVES: We report our experience implementing an algorithm for the detection of large vessel occlusion (LVO) for suspected stroke in the emergency setting, including its performance, and offer an explanation as to why it was poorly received by ra...

Knee osteoarthritis severity detection using deep inception transfer learning.

Computers in biology and medicine
Osteoarthritis (OA) is a prevalent condition resulting in physical limitations. Early detection of OA is critical to effectively manage this condition. However, the diagnosis of early-stage arthritis remains challenging. The Kellgren and Lawrence (KL...

Validation of AI-driven measurements for hip morphology assessment.

European journal of radiology
RATIONALE AND OBJECTIVES: Accurate assessment of hip morphology is crucial for the diagnosis and management of hip pathologies. Traditional manual measurements are prone to mistakes and inter- and intra-reader variability. Artificial intelligence (AI...

Multi-Energy Evaluation of Image Quality in Spectral CT Pulmonary Angiography Using Different Strength Deep Learning Spectral Reconstructions.

Academic radiology
RATIONALE AND OBJECTIVES: To evaluate and compare image quality of different energy levels of virtual monochromatic images (VMIs) using standard versus strong deep learning spectral reconstruction (DLSR) on dual-energy CT pulmonary angiogram (DECT-PA...

Multimodal Deep Learning Fusing Clinical and Radiomics Scores for Prediction of Early-Stage Lung Adenocarcinoma Lymph Node Metastasis.

Academic radiology
RATIONALE AND OBJECTIVES: To develop and validate a multimodal deep learning (DL) model based on computed tomography (CT) images and clinical knowledge to predict lymph node metastasis (LNM) in early lung adenocarcinoma.

Deep Learning Model for the Differential Diagnosis of Nasal Polyps and Inverted Papilloma by CT Images: A Multicenter Study.

Academic radiology
RATIONALE AND OBJECTIVES: Nasal polyps (NP) and inverted papilloma (IP) are benign tumors within the nasal cavity, each necessitating distinct treatment approaches. Herein, we investigate the utility of a deep learning (DL) model for distinguishing b...