Obstructive sleep apnea (OSA) is a prevalent sleep disorder with serious health risks, but its gold-standard diagnosis, polysomnography (PSG), is resource-intensive and limits accessibility. To address these limitations, we present DASH (Deep-learning-based Apnea Screening with AHI estimation), a web-integrated end-to-end system for apnea-hypopnea index (AHI) estimation using only single-lead electrocardiography (ECG). DASH consists of three stages: (1) per-segment apnea detection using a fine-tuned ECG-DualNet model, (2) AHI calculation via event aggregation and time normalization, and (3) severity classification based on AHI thresholds. Evaluated on a large-scale PSG-labeled dataset from Kangwon National University Hospital (KNUH), our model demonstrated robust performance, achieving an AUROC of 0.931 (segment-level) and an F1 score of 0.926 (severe-stage). In addition, we developed an interactive web interface for real-time screening and visualization. The results demonstrate that DASH offers a scalable, accurate, and accessible alternative to PSG for OSA detection using only ECG data.

Accurate localization of nasogastric tubes (NGTs) in chest X-rays is essential for safe clinical placement. Yet, standard deep learning models relying on overlap-based loss functions often neglect topological continuity, resulting in fragmented segmentations. We present a systematic benchmark of topologyaware methods-including advanced loss functions and the CoLeTra data augmentation-within the nnU-Net framework. Our best-performing configuration, combining Skeleton Recall Loss with CoLeTra augmentation, yielded consistent improvements across internal and external datasets. Notably, in challenging cases with overlapping chest tubes, it achieved relative gains of 10.9% in Dice Similarity Coefficient (DSC) and 10.4% in centerline Dice (clDice) compared to the baseline. These results demonstrate the effectiveness of topology-preserving learning for segmenting thin anatomical structures under real-world clinical conditions, without introducing additional inference overhead.

Neural Additive Models (NAMs) have recently demonstrated promising predictive performance while maintaining interpretability. However, their capacity is limited to capturing only first-order feature interactions, which restricts their effectiveness on real-world datasets. To address this limitation, we propose Higher-order Neural Additive Models (HONAMs), an interpretable machine learning model that effectively and efficiently captures feature interactions of arbitrary orders. HONAMs improve predictive accuracy without compromising interpretability, an essential requirement in high-stakes applications. This advantage of HONAM can help analyze and extract high-order interactions present in datasets. The source code for HONAM is publicly available at https://github.com/gim4855744/HONAM/.

Several recent studies have elucidated why knowledge distillation (KD) improves model performance. However, few have researched the other advantages of KD in addition to its improving model performance. In this study, we have attempted to show that KD enhances the interpretability as well as the accuracy of models. We measured the number of concept detectors identified in network dissection for a quantitative comparison of model interpretability. We attributed the improvement in interpretability to the class-similarity information transferred from the teacher to student models. First, we confirmed the transfer of class-similarity information from the teacher to student model via logit distillation. Then, we analyzed how class-similarity information affects model interpretability in terms of its presence or absence and degree of similarity information. We conducted various quantitative and qualitative experiments and examined the results on different datasets, different KD methods, and according to different measures of interpretability. Our research showed that KD models by large models could be used more reliably in various fields. The code is available at https://github.com/Rok07/KD_XAI.git.

Recently, the field of steganography has experienced rapid developments based on deep learning (DL). DL based steganography distributes secret information over all the available bits of the cover image, thereby posing difficulties in using conventional steganalysis methods to detect, extract or remove hidden secret images. However, our proposed framework is the first to effectively disable covert communications and transactions that use DL based steganography. We propose a DL based steganalysis technique that effectively removes secret images by restoring the distribution of the original images. We formulate a problem and address it by exploiting sophisticated pixel distributions and an edge distribution of images by using a deep neural network. Based on the given information, we remove the hidden secret information at the pixel level. We evaluate our technique by comparing it with conventional steganalysis methods using three public benchmarks. As the decoding method of DL based steganography is approximate (lossy) and is different from the decoding method of conventional steganography, we also introduce a new quantitative metric called the destruction rate (DT). The experimental results demonstrate performance improvements of 10–20$\%$in both the decoded rate and the DT.

In recent years, proposed studies on time-series anomaly detection (TAD) report high F1 scores on benchmark TAD datasets, giving the impression of clear improvements in TAD. However, most studies apply a peculiar evaluation protocol called point adjustment (PA) before scoring. In this paper, we theoretically and experimentally reveal that the PA protocol has a great possibility of overestimating the detection performance; even a random anomaly score can easily turn into a state-of-the-art TAD method. Therefore, the comparison of TAD methods after applying the PA protocol can lead to misguided rankings. Furthermore, we question the potential of existing TAD methods by showing that an untrained model obtains comparable detection performance to the existing methods even when PA is forbidden. Based on our findings, we propose a new baseline and an evaluation protocol. We expect that our study will help a rigorous evaluation of TAD and lead to further improvement in future researches.

Recent advances in next-generation sequencing technologies have led to the successful insertion of video information into DNA using synthesized oligonucleotides. Several attempts have been made to embed larger data into living organisms. This process of embedding messages is called steganography and it is used for hiding and watermarking data to protect intellectual property. In contrast, steganalysis is a group of algorithms that serves to detect hidden information from covert media. Various methods have been developed to detect messages embedded in conventional covert channels. However, conventional steganalysis algorithms are mostly limited to common covert media. Most common detection approaches, such as frequency analysis-based methods, often overlook important signals when directly applied to DNA steganography and are easily bypassed by recently developed steganography techniques. To address the limitations of conventional approaches, a sequence-learning-based malicious DNA sequence analysis method based on neural networks has been proposed. The proposed method learns intrinsic distributions and identifies distribution variations using a classification score to predict whether a sequence is to be a coding or non-coding sequence. Based on our experiments and results, we have developed a framework to safeguard security against DNA steganography.

Learning classifiers with imbalanced data can be strongly biased toward the majority class. To address this issue, several methods have been proposed using generative adversarial networks (GANs). Existing GAN-based methods, however, do not effectively utilize the relationship between a classifier and a generator. This article proposes a novel three-player structure consisting of a discriminator, a generator, and a classifier, along with decision boundary regularization. Our method is distinctive in which the generator is trained in cooperation with the classifier to provide minority samples that gradually expand the minority decision region, improving performance for imbalanced data classification. The proposed method outperforms the existing methods on real data sets as well as synthetic imbalanced data sets.

Drugs are classified according to their biological and chemical reactions, and the systems that they target. Thus, an accurate and efficient prediction method for drug class discovery would reveal key properties of candidate drugs, significantly conserving time and resources in drug repositioning and design. Previous approaches, based on data mining or statistics, required complicated feature construction in advance. Knowing that deep learning can identifying patterns in high-dimensional datasets without elaborate feature selection or engineering, we constructed a model for predicting drug classes using deep neural networks - with biological and chemical structure data. Our proposed model outperforms previous learning-based methods in terms of prediction accuracy.

Detecting major system anomalies with observed multivariate time series requires not only the characteristics of each time series but also the status of the entire time series dynamics. Therefore, we propose a method that can detect substantial anomalies by generating a transfer network and an influence network from a multivariate time series. To form a transfer network, each vertex represents a single time series. Each edge indicates the strength of the information flow between each pair of time series using transfer entropy. With the transfer network, we exploit the random walk approach to calculate the affinity score between two vertices and create an influence network that reflects both the direct and indirect influences. In our experiment, we show the efficacy of the proposed method using simple synthetic time series networks and the real data set such as world stock indices and key performance indicators of the SAP HANA in-memory database system.

To assess the genetic diversity of an environmental sample in metagenomics studies, the amplicon sequences of 16s rRNA genes need to be clustered into operational taxonomic units (OTUs). Many existing tools for OTU clustering trade off between accuracy and computational efficiency. We propose a novel OTU clustering algorithm, hc-OTU, which achieves high accuracy and fast runtime by exploiting homopolymer compaction and k-mer profiling to significantly reduce the computing time for pairwise distances of amplicon sequences. We compare the proposed method with other widely used methods, including UCLUST, CD-HIT, MOTHUR, ESPRIT, ESPRIT-TREE, and CLUSTOM, comprehensively, using nine different experimental datasets and many evaluation metrics, such as normalized mutual information, adjusted Rand index, measure of concordance, and F-score. Our evaluation reveals that the proposed method achieves a level of accuracy comparable to the respective accuracy levels of MOTHUR and ESPRIT-TREE, two widely used OTU clustering methods, while delivering orders-of-magnitude speedups.

MicroRNAs (miRNAs) are small non-coding ribonucleic acids (RNAs) which play key roles in post-transcriptional gene regulation. Direct identification of mature miRNAs is infeasible due to their short lengths, and researchers instead aim at identifying precursor miRNAs (pre-miRNAs). Many of the known pre-miRNAs have distinctive stem-loop secondary structure, and structure-based filtering is usually the first step to predict the possibility of a given sequence being a pre-miRNA. To identify new pre-miRNAs that often have non-canonical structure, however, we need to consider additional features other than structure. To obtain such additional characteristics, existing computational methods rely on manual feature extraction, which inevitably limits the efficiency, robustness, and generalization of computational identification. To address the limitations of existing approaches, we propose a pre-miRNA identification method that incorporates (1) a deep recurrent neural network (RNN) for automated feature learning and classification, (2) multimodal architecture for seamless integration of prior knowledge (secondary structure), (3) an attention mechanism for improving long-term dependence modeling, and (4) an RNN-based class activation mapping for highlighting the learned representations that can contrast pre-miRNAs and non-pre-miRNAs. In our experiments with recent benchmarks, the proposed approach outperformed the compared state-of-the-art alternatives in terms of various performance metrics.