The efficient and automatic detection of chest abnormalities is vital for the auxiliary diagnosis of medical images. Many studies utilize computer vision and deep learning approaches involving symmetry and asymmetry concepts to detect chest abnormalities, and achieve promising findings. However, an accurate instance-level and multi-label detection of abnormalities in chest X-rays remains a significant challenge. Here, a novel anomaly detection method for symmetric chest X-rays using dual-attention and multi-scale feature fusion is proposed. Three aspects of our method should be noted in comparison with the previous approaches. We improved the deep neural network with channel-dimensional and spatial-dimensional attention to capture the abundant contextual features. We then used an optimized multi-scale learning framework for feature fusion to adapt to the scale variation in the abnormalities. Considering the influence of the data imbalance and other factors, we introduced a seesaw loss function to flexibly adjust the sample weights and enhance the model learning efficiency. The rigorous experimental evaluation of a public chest X-ray dataset with fourteen different types of abnormalities demonstrates that our model has a mean average precision of 0.362 and outperforms existing methods.
In minimally invasive laparoscopic surgery, accurate detection of the location and specific category of surgical tools assists the surgeon in making a correct objective judgment of the current surgical outcome and alerts to these possible adverse medical events. In this paper, an analyzing error method is proposed for revealing the specific sources of error in previous work. Then we confirm the main bottlenecks of the current methods with a high level of missing error and localization error. To reduce these errors, we render the backbone with a three‐dimensional attention mechanism and adopt a double‐headed detection head design to replace the single detection head. Moreover, we propose an enhanced multilayer perceptron called Mconv to enhance the localization branch of the double‐headed detection head. Our experiments evaluate our improved approach by our proposed error analysis method on a pubic dataset m2cai16‐tool‐locations, showing that our method yield remarkable detection accuracy than others.
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