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Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization is a peer-reviewed journal published by Taylor & Francis, covering AI in cancer detection, Medical Image Segmentation Techniques, Radiomics and Machine Learning in Medical Imaging.
| Publisher | Taylor & Francis |
|---|---|
| Reference style | Author–year (Chicago, T&F) Author–year — (Smith, 2023) in the text Smith, Ada, Ben Jones, and Cara Lee. 2023. "A Representative Article Title." Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization 12 (3): 45–58.
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| Publishes research in | AI in cancer detection Medical Image Segmentation Techniques Radiomics and Machine Learning in Medical Imaging Brain Tumor Detection and Classification Digital Imaging for Blood Diseases |
| ISSN | 2168-1163 |
| Citation impact (2-yr) | 5.33 |
| h-index | 37 |
| i10-index | 227 |
| Total citations | 8,524 |
| Open access | Yes |
| Top institutions publishing here | Centre National de la Recherche Scientifique |
| Journal website | www.tandfonline.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
This paper concerns automated cell counting and detection in microscopy images. The approach we take is to use convolutional neural networks (CNNs) to regress a cell spatial density map across the image. This is applicable to situations where traditional single-cell segmentation-based methods do not work well due to cell clumping or overlaps. We make the…
Interstitial lung diseases (ILD) involve several abnormal imaging patterns observed in computed tomography (CT) images. Accurate classification of these patterns plays a significant role in precise clinical decision making of the extent and nature of the diseases. Therefore, it is important for developing automated pulmonary computer-aided detection systems. Conventionally, this task relies on experts' manual…
A recent study from GLOBOCAN disclosed that during 2018 two million women worldwide had been diagnosed with breast cancer. Currently, mammography, magnetic resonance imaging, ultrasound, and biopsies are the main screening techniques, which require either, expensive devices or personal qualified; but some countries still lack access due to economic, social, or cultural issues. As an…
An effective similarity measure for multi-modal images is crucial for medical image fusion in many clinical applications. The underlining correlation across modalities is usually too complex to be modelled by intensity-based statistical metrics. Therefore, approaches of learning a similarity metric are proposed in recent years. In this work, we propose a novel deep similarity learning…
The rapid identification and categorisation of breast cancers using low-contrast MRI images presents a significant challenge due to the disease’s prevalence among women of all ages. Nowadays, it is very difficult to classify and generalise models from low-contrast MRI datasets due to the prevalence of class imbalance caused by a wide range of symptoms and…
