Convolutional neural networks in deep learning models have dominated the recent image recognition works. But the lack of capacity to maintain spatial invariance makes identification of micronucleus cells as a classic task in digital pathology still a challenge task. In this paper, a novel convolutional neural network for feature maps spatial transformation (FSTCNN) is proposed, which incorporates a Spatial Transformer Network. Our model allows the spatial manipulation of data within the network, provides the ability of active spatial transformation for neural network without any extra supervision. We compared the results of inserting STN into different convolutional layers and found that such a network can transform the input image more steadily, correct the image to one certain position, make it fill the whole screen to create a better environment for image recognition. The results show a distinct advantage over other convolutional neural networks for medical image recognition.
The development of convolutional neural network has brought great achievements to image classification in recent years. However, the classification performance is good only for natural images rather than medical images. An important reason is that the medical image database used for training is always deficient. So how to use these limited data to acquire more extensive features has become a hot research focus. In this paper, we first update the order and number of the whole training data every time in active and incremental fine-tuning. Then we set different contribution rate for the data selected in our model, which based on the information quantity of the data in training stage and make our model converge steadily. After that, a pre-trained model and our preprocessed datasets are employed, which allows us to further fine-tune our models. The experiments evaluated on two different biomedical datasets shows that our model can achieve promising results.
Encoder-decoder framework attracts great interests in image caption. It focuses on the extraction of low-level features and achieves good results. The performance can be further improved if high-level semantics are considered. In this work, we propose a new image caption model incorporating high-level semantic features through an revised Convolutional Neural Network(CNN). Both the low-level image features and high-level semantic features are fed into the Long-Short Term Memory networks(LSTMs) to acquire natural sentence descriptions. We show in a number of experiments on Flickr8K and Flickr30K datasets that our method outperforms most standard network baseline for image caption.