Rethinking Separable Convolutional Encoders for End-to-End Semantic Image Segmentation

Abstract

With the development of science and technology, the middle volume and neural network in the semantic image segmentation of the codec show good development prospects. Its advantage is that it can extract richer semantic features, but this will cause high costs. In order to solve this problem, this article mainly introduces the codec based on a separable convolutional neural network for semantic image segmentation. This article proposes a codec based on a separable convolutional neural network for semantic image segmentation research methods, including the traditional convolutional neural network hierarchy into a separable convolutional neural network, which can reduce the cost of image data segmentation and improve processing efficiency. Moreover, this article builds a separable convolutional neural network codec structure and designs a semantic segmentation process, so that the codec based on a separable convolutional neural network is used for semantic image segmentation research experiments. The experimental results show that the average improvement of the dataset by the improved codec is 0.01, which proves the effectiveness of the improved SegProNet. The smaller the number of training set samples, the more obvious the performance improvement.

Anatomy segmentation in laparoscopic surgery: comparison of machine learning and human expertise – an experimental study

“…IoU, F1 score, precision, recall, specificity) that are commonly used to evaluate segmentation performance, the structure-specific models and the combined models provided comparable segmentation performances on the internal test dataset. Interpretation of such metrics of overlap, however, represents a major challenge in computer vision applications in medical domains such as dermatology and endoscopy 39 – 41 as well as non-medical domains such as autonomous driving 42 . In the specific use case of laparoscopic surgery, evidence suggests that such technical metrics alone are not sufficient to characterize the clinical potential and utility of segmentation algorithms 37 , 43 .…”

Retracted: Rethinking Separable Convolutional Encoders for End‐to‐End Semantic Image Segmentation

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”