Majority or Minority: Data Imbalance Learning Method for Named Entity Recognition

Data imbalance presents a significant challenge in various machine learning (ML) tasks, particularly named entity recognition (NER) within natural language processing (NLP). NER exhibits a data imbalance with a long-tail distribution, featuring numerous minority classes (i.e., entity classes) and a single majority class (i.e., O-class). The imbalance leads to the misclassifications of the entity classes as the O-class. To tackle the imbalance, we propose a simple and effective learning method, named majority or minority (MoM) learning. MoM learning incorporates the loss computed only for samples whose ground truth is the majority class (i.e., the O-class) into the loss of the conventional ML model. Evaluation experiments on four NER datasets (Japanese and English) showed that MoM learning improves prediction performance of the minority classes, without sacrificing the performance of the majority class and is more effective than widely known and state-of-the-art methods. We also evaluated MoM learning using frameworks as sequential labeling and machine reading comprehension, which are commonly used in NER. Furthermore, MoM learning has achieved consistent performance improvements regardless of language, model, or framework.

Towards Robust Plant Disease Diagnosis with Hard-sample Re-mining Strategy

With rich annotation information, object detection-based automated plant disease diagnosis systems (e.g., YOLO-based systems) often provide advantages over classification-based systems (e.g., EfficientNet-based), such as the ability to detect disease locations and superior classification performance. One drawback of these detection systems is dealing with unannotated healthy data with no real symptoms present. In practice, healthy plant data appear to be very similar to many disease data. Thus, those models often produce mis-detected boxes on healthy images. In addition, labeling new data for detection models is typically time-consuming. Hard-sample mining (HSM) is a common technique for re-training a model by using the mis-detected boxes as new training samples. However, blindly selecting an arbitrary amount of hard-sample for re-training will result in the degradation of diagnostic performance for other diseases due to the high similarity between disease and healthy data. In this paper, we propose a simple but effective training strategy called hard-sample re-mining (HSReM), which is designed to enhance the diagnostic performance of healthy data and simultaneously improve the performance of disease data by strategically selecting hard-sample training images at an appropriate level. Experiments based on two practical in-field eight-class cucumber and ten-class tomato datasets (42.7K and 35.6K images) show that our HSReM training strategy leads to a substantial improvement in the overall diagnostic performance on large-scale unseen data. Specifically, the object detection model trained using the HSReM strategy not only achieved superior results as compared to the classification-based state-of-the-art EfficientNetV2-Large model and the original object detection model, but also outperformed the model using the HSM strategy.

A Practical Framework for Unsupervised Structure Preservation Medical Image Enhancement

Medical images are extremely valuable for supporting medical diagnoses. However, in practice, low-quality (LQ) medical images, such as images that are hazy/blurry, have uneven illumination, or are out of focus, among others, are often obtained during data acquisition. This leads to difficulties in the screening and diagnosis of medical diseases. Several generative adversarial networks (GAN)-based image enhancement methods have been proposed and have shown promising results. However, there is a quality-originality trade-off among these methods in the sense that they produce visually pleasing results but lose the ability to preserve originality, especially the structural inputs. Moreover, to our knowledge, there is no objective metric in evaluating the structure preservation of medical image enhancement methods in unsupervised settings due to the unavailability of paired ground-truth data. 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. The LaSSIM metric does not require clean reference images and has been shown to be superior to SSIM in capturing image structural changes under image degradations, such as strong blurring on different datasets. The experiments demonstrated that our LaMEGAN achieves a satisfactory balance between quality and originality, with robust structure preservation performance while generating compelling visual results with very high image quality scores. The code will be made available at https://github.com/AillisInc/USPMIE.

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. Our framework first determines the quality of images and then generates captions using only those images that are determined to be of high quality. The user is notified by the flaws feature to retake if image quality is low, and this cycle is repeated until the input image is deemed to be of high quality. As a component of the framework, we trained and evaluated a low-quality image detection model that simultaneously learns difficulty in recognizing images and individual flaws, and we demonstrated that our proposal can explain the reasons for flaws with a sufficient score. We also evaluated a dataset with low-quality images removed by our framework and found improved values for all four common metrics (e.g., BLEU-4, METEOR, ROUGE-L, CIDEr), confirming an improvement in general-purpose image captioning capability. Our framework would assist the visually impaired, who have difficulty judging image quality.

Loc-VAE: Learning Structurally Localized Representation from 3D Brain MR Images for Content-Based Image Retrieval

Content-based image retrieval (CBIR) systems are an emerging technology that supports reading and interpreting medical images. Since 3D brain MR images are high dimensional, dimensionality reduction is necessary for CBIR using machine learning techniques. In addition, for a reliable CBIR system, each dimension in the resulting low-dimensional representation must be associated with a neurologically interpretable region. We propose a localized variational autoencoder (Loc-VAE) that provides neuroanatomically interpretable low-dimensional representation from 3D brain MR images for clinical CBIR. 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. The proposed Loc-VAE is capable of acquiring representation that preserves disease features and is highly localized, even under high-dimensional compression ratios (4096:1). The low-dimensional representation obtained by Loc-VAE improved the locality measure of each dimension by 4.61 points compared to naive $\beta$-VAE, while maintaining comparable brain reconstruction capability and information about the diagnosis of Alzheimer's disease.

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. Typical methods for extracting important information from multimodal data rely on a mid-fusion architecture that combines the feature representations from multiple encoders. However, as the number of modalities increases, several potential problems with the mid-fusion model structure arise, such as an increase in the dimensionality of the concatenated multimodal features and missing modalities. To address these problems, we propose a new concept that considers multimodal inputs as a set of sequences, namely, deep multimodal sequence sets (DM$^2$S$^2$). Our set-aware concept consists of three components that capture the relationships among multiple modalities: (a) a BERT-based encoder to handle the inter- and intra-order of elements in the sequences, (b) intra-modality residual attention (IntraMRA) to capture the importance of the elements in a modality, and (c) inter-modality residual attention (InterMRA) to enhance the importance of elements with modality-level granularity further. Our concept exhibits performance that is comparable to or better than the previous set-aware models. Furthermore, we demonstrate that the visualization of the learned InterMRA and IntraMRA weights can provide an interpretation of the prediction results.

Expressions Causing Differences in Emotion Recognition in Social Networking Service Documents

It is often difficult to correctly infer a writer's emotion from text exchanged online, and differences in recognition between writers and readers can be problematic. In this paper, we propose a new framework for detecting sentences that create differences in emotion recognition between the writer and the reader and for detecting the kinds of expressions that cause such differences. 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. The detector, based on a Japanese SNS-document dataset with emotion labels annotated by both the writer and three readers of the social networking service (SNS) documents, detected "hidden-anger sentences" with AUC = 0.772; these sentences gave rise to differences in the recognition of anger. Because SNS documents contain many sentences whose meaning is extremely difficult to interpret, by analyzing the sentences detected by this detector, we obtained several expressions that appear characteristically in hidden-anger sentences. The detected sentences and expressions do not convey anger explicitly, and it is difficult to infer the writer's anger, but if the implicit anger is pointed out, it becomes possible to guess why the writer is angry. Put into practical use, this framework would likely have the ability to mitigate problems based on misunderstandings.

Gastric Cancer Detection from X-ray Images Using Effective Data Augmentation and Hard Boundary Box Training

X-ray examination is suitable for screening of gastric cancer. Compared to endoscopy, which can only be performed by doctors, X-ray imaging can also be performed by radiographers, and thus, can treat more patients. However, the diagnostic accuracy of gastric radiographs is as low as 85%. To address this problem, highly accurate and quantitative automated diagnosis using machine learning needs to be performed. This paper proposes a diagnostic support method for detecting gastric cancer sites from X-ray images with high accuracy. The two new technical proposal of the method are (1) stochastic functional gastric image augmentation (sfGAIA), and (2) hard boundary box training (HBBT). The former is a probabilistic enhancement of gastric folds in X-ray images based on medical knowledge, whereas the latter is a recursive retraining technique to reduce false positives. We use 4,724 gastric radiographs of 145 patients in clinical practice and evaluate the cancer detection performance of the method in a patient-based five-group cross-validation. The proposed sfGAIA and HBBT significantly enhance the performance of the EfficientDet-D7 network by 5.9% in terms of the F1-score, and our screening method reaches a practical screening capability for gastric cancer (F1: 57.8%, recall: 90.2%, precision: 42.5%).

Disease-oriented image embedding with pseudo-scanner standardization for content-based image retrieval on 3D brain MRI

To build a robust and practical content-based image retrieval (CBIR) system that is applicable to a clinical brain MRI database, we propose a new framework -- Disease-oriented image embedding with pseudo-scanner standardization (DI-PSS) -- that consists of two core techniques, data harmonization and a dimension reduction algorithm. Our DI-PSS uses skull stripping and CycleGAN-based image transformations that map to a standard brain followed by transformation into a brain image taken with a given reference scanner. Then, our 3D convolutioinal autoencoders (3D-CAE) with deep metric learning acquires a low-dimensional embedding that better reflects the characteristics of the disease. The effectiveness of our proposed framework was tested on the T1-weighted MRIs selected from the Alzheimer's Disease Neuroimaging Initiative and the Parkinson's Progression Markers Initiative. We confirmed that our PSS greatly reduced the variability of low-dimensional embeddings caused by different scanner and datasets. 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. These properties allow DI-PSS to generate lower dimensional representations that are more amenable to disease classification. In AD and CN classification experiments based on spectral clustering, PSS improved the average accuracy and macro-F1 by 6.2% and 10.7%, respectively. Given the potential of the DI-PSS for harmonizing images scanned by MRI scanners that were not used to scan the training data, we expect that the DI-PSS is suitable for application to a large number of legacy MRIs scanned in heterogeneous environments.

Making Attention Mechanisms More Robust and Interpretable with Virtual Adversarial Training for Semi-Supervised Text Classification

We propose a new general training technique for attention mechanisms based on virtual adversarial training (VAT). VAT can compute adversarial perturbations from unlabeled data in a semi-supervised setting for the attention mechanisms that have been reported in previous studies to be vulnerable to perturbations. Empirical experiments reveal that our technique (1) provides significantly better prediction performance compared to not only conventional adversarial training-based techniques but also VAT-based techniques in a semi-supervised setting, (2) demonstrates a stronger correlation with the word importance and better agreement with evidence provided by humans, and (3) gains in performance with increasing amounts of unlabeled data.