Search Results for author:
Found 27 papers, 7 papers with code
Lesion Border Detection in Dermoscopy Images Using Ensembles of Thresholding Methods
Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions.
Skin lesion classification with ensemble of squeeze-and-excitation networks and semi-supervised learning
In this report, we introduce the outline of our system in Task 3: Disease Classification of ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection.
End-to-End Text Classification via Image-based Embedding using Character-level Networks
For analysing and/or understanding languages having no word boundaries based on morphological analysis such as Japanese, Chinese, and Thai, it is desirable to perform appropriate word segmentation before word embeddings.
Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creative
Furthermore, conditional attention focuses attention of each ad creative with the consideration of its genre and target gender, thus improving conversion prediction accuracy.
A comparable study: Intrinsic difficulties of practical plant diagnosis from wide-angle images
Practical automated detection and diagnosis of plant disease from wide-angle images (i. e. in-field images containing multiple leaves using a fixed-position camera) is a very important application for large-scale farm management, in view of the need to ensure global food security.
AOP: An Anti-overfitting Pretreatment for Practical Image-based Plant Diagnosis
As a result, overfitting due to latent similarities in the dataset often occurs, and the diagnostic performance on real unseen data (e, g.
Super-Resolution for Practical Automated Plant Disease Diagnosis System
Automated plant diagnosis using images taken from a distance is often insufficient in resolution and degrades diagnostic accuracy since the important external characteristics of symptoms are lost.
Efficient feature embedding of 3D brain MRI images for content-based image retrieval with deep metric learning
Content-based image retrieval (CBIR) is an emerging technology for reducing this burden by supporting the reading of medical images.
LeafGAN: An Effective Data Augmentation Method for Practical Plant Disease Diagnosis
LeafGAN generates a wide variety of diseased images via transformation from healthy images, as a data augmentation tool for improving the performance of plant disease diagnosis.
AraDIC: Arabic Document Classification using Image-Based Character Embeddings and Class-Balanced Loss
Classical and some deep learning techniques for Arabic text classification often depend on complex morphological analysis, word segmentation, and hand-crafted feature engineering.
Attention Meets Perturbations: Robust and Interpretable Attention with Adversarial Training
To overcome the vulnerability to perturbations in the mechanism, we are inspired by adversarial training (AT), which is a powerful regularization technique for enhancing the robustness of the models.
LASSR: Effective Super-Resolution Method for Plant Disease Diagnosis
The collection of high-resolution training data is crucial in building robust plant disease diagnosis systems, since such data have a significant impact on diagnostic performance.
Text Classification through Glyph-aware Disentangled Character Embedding and Semantic Sub-character Augmentation
We propose a new character-based text classification framework for non-alphabetic languages, such as Chinese and Japanese.
MIINet: An Image Quality Improvement Framework for Supporting Medical Diagnosis
Medical images have been indispensable and useful tools for supporting medical experts in making diagnostic decisions.
Bulk Production Augmentation Towards Explainable Melanoma Diagnosis
Although highly accurate automated diagnostic techniques for melanoma have been reported, the realization of a system capable of providing diagnostic evidence based on medical indices remains an open issue because of difficulties in obtaining reliable training data.
Making Attention Mechanisms More Robust and Interpretable with Virtual Adversarial Training
That is, even if the model using our VAT-based technique is trained on unlabeled data from a source other than the target task, both the prediction performance and model interpretability can be improved.
Disease-oriented image embedding with pseudo-scanner standardization for content-based image retrieval on 3D brain MRI
Compared with the baseline condition, our PSS reduced the variability in the distance from Alzheimer's disease (AD) to clinically normal (CN) and Parkinson disease (PD) cases by 15. 8-22. 6% and 18. 0-29. 9%, respectively.
Practical X-ray Gastric Cancer Screening Using Refined Stochastic Data Augmentation and Hard Boundary Box Training
In gastric cancer screening, X-rays can be performed by radiographers, allowing them to see far more patients than endoscopy, which can only be performed by physicians.
Ad Creative Discontinuation Prediction with Multi-Modal Multi-Task Neural Survival Networks
To improve the prediction performance for the two different types of discontinuations and for the ad creatives that contribute to sales, we introduce two new techniques: (1) a two-term estimation technique with multi-task learning and (2) a click-through rate-weighting technique for the loss function.
Expressions Causing Differences in Emotion Recognition in Social Networking Service Documents
The proposed framework consists of a bidirectional encoder representations from transformers (BERT)-based detector that detects sentences causing differences in emotion recognition and an analysis that acquires expressions that characteristically appear in such sentences.
DM$^2$S$^2$: Deep Multi-Modal Sequence Sets with Hierarchical Modality Attention
There is increasing interest in the use of multimodal data in various web applications, such as digital advertising and e-commerce.
Loc-VAE: Learning Structurally Localized Representation from 3D Brain MR Images for Content-Based Image Retrieval
Loc-VAE is based on $\beta$-VAE with the additional constraint that each dimension of the low-dimensional representation corresponds to a local region of the brain.
Feedback is Needed for Retakes: An Explainable Poor Image Notification Framework for the Visually Impaired
We propose a simple yet effective image captioning framework that can determine the quality of an image and notify the user of the reasons for any flaws in the image.
A Practical Framework for Unsupervised Structure Preservation Medical Image Enhancement
In this study, we propose a framework for practical unsupervised medical image enhancement that includes (1) a non-reference objective evaluation of structure preservation for medical image enhancement tasks called Laplacian structural similarity index measure (LaSSIM), which is based on SSIM and the Laplacian pyramid, and (2) a novel unsupervised GAN-based method called Laplacian medical image enhancement (LaMEGAN) to support the improvement of both originality and quality from LQ images.
Towards Robust Plant Disease Diagnosis with Hard-sample Re-mining Strategy
Hard-sample mining (HSM) is a common technique for re-training a model by using the mis-detected boxes as new training samples.
Majority or Minority: Data Imbalance Learning Method for Named Entity Recognition
This imbalance leads to misclassifications of the entity classes as the O-class.
Validity-Based Sampling and Smoothing Methods for Multiple Reference Image Captioning
In image captioning, multiple captions are often provided as ground truths, since a valid caption is not always uniquely determined.
