# Journal of Imaging Informatics in Medicine

**Type:** Journals  
**Canonical URL:** https://scholariq.org/journals/journal-of-imaging-informatics-in-medicine/

## Facts

| Field | Value |
| --- | --- |
| Open Access | false |
| ISSN-L | 0897-1889 |
| ISSNs | 0897-1889,1618-727X,2948-2925,2948-2933 |
| OpenAlex ID | https://openalex.org/S62275304 |
| Publisher | Springer Science+Business Media |

## Journal papers

- [Unsupervised Deep Anomaly Detection in Chest Radiographs](https://scholariq.org/papers/unsupervised-deep-anomaly-detection-in-chest-radiographs/)
- [Efficacy of a Checklist-Style Structured Radiology Reporting Template in Reducing Resident Misses on Cervical Spine Computed Tomography Examinations](https://scholariq.org/papers/efficacy-of-a-checklist-style-structured-radiology-reporting-template-in/)
- [Properties of Noise in Positron Emission Tomography Images Reconstructed with Filtered-Backprojection and Row-Action Maximum Likelihood Algorithm](https://scholariq.org/papers/properties-of-noise-in-positron-emission-tomography-images-reconstructed-with/)
- [Image Retake Analysis in Digital Radiography Using DICOM Header Information](https://scholariq.org/papers/image-retake-analysis-in-digital-radiography-using-dicom-header-information/)
- [MobileNet-V2: An Enhanced Skin Disease Classification by Attention and Multi-Scale Features](https://scholariq.org/papers/mobilenet-v2-an-enhanced-skin-disease-classification-by-attention-and-multi/)
- [Improving the Efficacy of ACR TI-RADS Through Deep Learning-Based Descriptor Augmentation](https://scholariq.org/papers/improving-the-efficacy-of-acr-ti-rads-through-deep-learning-based-descriptor/)
- [Explained Deep Learning Framework for COVID-19 Detection in Volumetric CT Images Aligned with the British Society of Thoracic Imaging Reporting Guidance: A Pilot Study](https://scholariq.org/papers/explained-deep-learning-framework-for-covid-19-detection-in-volumetric-ct-images/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.