经过二轮审稿，第一轮大修三个人提出了不少意见。但是这个审稿人2是个奇葩，英语表示存在歧义，说的不明不白的，到了第二轮才说，这么点数据用深度学习效果不行，关键我也不是这么点数据，我们这个方向都是进行样本裁剪进行增强后都有10w+的数据，他也不看我文中的。这是他两次的审稿意见，很奇葩。
第一次意见：
in majority scenarios of medical image classification, acquiring sufficient amount of labeled images is very difficult. as mentioned in the manuscript (line 164, page 6), there are 468 images in total for this study. with this size of data, it could be questionable to justify the effectiveness of deep networks’ capability for feature extraction.

第二次意见：都是对应点，就这个问题他不满意
for example, in my first comment, i argued why to use the deep networks in this medical image classification, since the training samples are so little. in the authors’ response, i did not find any justification or arguments that can explain away this question. however, the deep networks are the main techniques used in the manuscript, and this should not be ignored.

overall, the manuscript is hard to follow to me. the manuscript’s motivation of using feature fusion also seems to be questionable.

therefore, i regret that i am not able to recommend this manuscript to be considered for publication in sensors. 返回小木虫查看更多