Abdominal ultrasound examination is considered to be highly challenging because of its need to diagnose from moving images taken while handling devices. Previous method consisted of a two-stage inference step where tumors in the input ultrasound image was detected and then the cropped area was classified. However, this previous method may be inaccurate because the tumour detection model is not suitable due to the inability to use global features for classification against the cropped diagnostic image. Therefore, we propose a method that uses SimSiam to pretrain CenterNet and infer using only a single model. The proposed method improves classification accuracy by 3%, and improves memory usage and inference speed by 50% and 33% respectively.
Although an attention mechanism is reasonable for generating image captions, how to obtain ideal image regions within the mechanism is a problem in practice due to the difficulty of its calculation between image and text data. In order to improve the attention modules for image captioning, we propose an algorithm for handling a pixel-wise semantic information, which is obtained as the outputs of semantic segmentation. The proposed method puts the pixel-wise semantic information into the attention modules for image captioning together with input text data and image features. We conducted evaluation experiments and confirmed that our method could obtain more reasonable weighted image features and better image captions with a BLEU-4 score of 0.306 than its original attention model with a BLEU-4 score of 0.243.
The ultrasound examination is a difficult operation because a doctor not only operates an ultrasound scanner but also interprets images in rea time, which may increase the risk of overlooking tumors. To prevent that, we study a liver tumor detection method using convolutional neural networks toward realizing computer-assisted diagnosis systems. In this paper, we propose a liver tumor detection method within a false positive reduction framework. The proposed method uses YOLOv3 [1] in order to find tumor candidate regions in real-time, and also uses VGG16 [2] to reduce false positives. The proposed method using YOLOv3 [1] and VGG16 [2] achieved an F-measure of 0.837, which showed the effectiveness of the proposed method for liver tumor detection. Future work includes the collection of training data from more hospitals and their effective use for improving the detection accuracy.
The documents of the government-general of Taiwan recorded from 1895 to 1945 contain the whole of Japanese official documents before the end of the WW2, and have great historic value. The characters in the documents, however, are illegible because they were written by hand with a brush. It is labor-intensive work for historians or scholars to understand the documents. We propose a method for character recognition of these documents by using a convolutional neural network and also conduct to solve the problem of imbalanced learning data. Experimental results show that the top-1 and the top10 accuracies were 89.48% and 98.10%, respectively.
Wireless capsule endoscopy (WCE) is a new clinical technology permitting the visualization of the small bowel,
the most difficult segment of the digestive tract. The major drawback of this technology is the high amount
of time for video diagnosis. In this study, we propose a method for informative frame detection by isolating
useless frames that are substantially covered by turbid fluids or their contamination with other materials, e.g.,
faecal, semi-processed or unabsorbed foods etc. Such materials and fluids present a wide range of colors, from
brown to yellow, and/or bubble-like texture patterns. The detection scheme, therefore, consists of two stages:
highly contaminated non-bubbled (HCN) frame detection and significantly bubbled (SB) frame detection. Local
color moments in the Ohta color space are used to characterize HCN frames, which are isolated by the Support
Vector Machine (SVM) classifier in Stage-1. The rest of the frames go to the Stage-2, where Laguerre gauss
Circular Harmonic Functions (LG-CHFs) extract the characteristics of the bubble-structures in a multi-resolution
framework. An automatic segmentation method is designed to extract the bubbled regions based on local absolute
energies of the CHF responses, derived from the grayscale version of the original color image. Final detection of
the informative frames is obtained by using threshold operation on the extracted regions. An experiment with
20,558 frames from the three videos shows the excellent average detection accuracy (96.75%) by the proposed
method, when compared with the Gabor based- (74.29%) and discrete wavelet based features (62.21%).
This paper presents a method for automated detection of liver cancer regions based on transition of density at each point obtained from multi-phase X-ray CT images. For describing transition of density, two kinds of feature vectors named Density Transition (DT) and Density Change Transition (DCT) are introduced. DCT is used for extraction of cancer candidates and DT is used for suppression of false candidates. In the experiments using 14 real abdominal CT images with cancer, it was shown that the detection rate was 100% and the number of false-positives was 0.71 regions per case.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.