as well as NHS staff after scientists developed a programme which mimics the gaze of radiologists reading medical images.
It is hoped the groundbreaking tech will help to address a UK-wide shortage of radiologists through training and education applications. Dr Hantao Liu, one of the study’s co-authors, said: “With all of the challenges facing the NHS, it is important that we look to data science and AI for possible solutions. This doesn’t mean replacing people with robots but instead demonstrates how machine learning can support and augment the work of clinical professionals.
Dr Richard White, a Consultant Radiologist at UHW who participated in the study, said: “There’s so much data involved in radiology that I think it’s best we make use of it and the expertise available.
We have summarized this news so that you can read it quickly. If you are interested in the news, you can read the full text here. Read more:
Health Health Latest News, Health Health Headlines
Similar News:You can also read news stories similar to this one that we have collected from other news sources.
Predicting Radiologists' Gaze with Computational Saliency Models in Mammogram ReadingPrevious studies have shown that there is a strong correlation between radiologists' diagnoses and their gaze when reading medical images. The extent to which gaze is attracted by content in a visual scene can be characterised as visual saliency. There is a potential for the use of visual saliency in computer-aided diagnosis in radiology. However, little is known about what methods are effective for diagnostic images, and how these methods could be adapted to address specific applications in diagnostic imaging. In this study, we investigate 20 state-of-the-art saliency models including 10 traditional models and 10 deep learning-based models in predicting radiologists' visual attention while reading 196 mammograms. We found that deep learning-based models represent the most effective type of methods for predicting radiologists' gaze in mammogram reading; and that the performance of these saliency models can be significantly improved by transfer learning. In particular, an enhanced model can be achieved by pre-training the model on a large-scale natural image saliency dataset and then fine-tuning it on the target medical image dataset. In addition, based on a systematic selection of backbone networks and network architectures, we proposed a parallel multi-stream encoded model which outperforms the state-of-the-art approaches for predicting saliency of mammograms.
Source: medical_xpress - 🏆 101. / 51 Read more »