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Researcher profile

Ke Yu

Ke Yu contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Machine LearningComputer VisionArtificial Intelligenceastro-ph.SReess.IVBiomoleculesMolecular Networksphysics.comp-phQuantitative Methods

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Published work

11 published item(s)

preprint2026arXiv

HELIX: Hybrid Encoding with Learnable Identity and Cross-dimensional Synthesis for Time Series Imputation

Time series imputation benefits from leveraging cross-feature correlations, yet existing attention-based methods re-discover feature relationships at each layer, lacking persistent anchors to maintain consistent representations. To address this, we propose HELIX, which assigns each feature a learnable feature identity, a persistent embedding that captures intrinsic semantic properties throughout the network. Unlike graph-based methods that rely on predefined topology and assume homogeneous spatial relationships, HELIX learns arbitrary feature dependencies end-to-end from temporal co-variation, naturally handling datasets where features mix spatial locations with semantic variables. Integrated with hybrid temporal-feature attention, HELIX achieves the state-of-the-art performance, surpassing all 16 baselines on 5 public datasets across 21 experimental settings in our evaluation. Furthermore, our mechanistic analysis reveals that HELIX aligns learned feature identities and dependencies with latent physical and semantic structure progressively across layers, demonstrating that it more effectively translates cross-feature structure into imputation accuracy.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Anatomy-Guided Weakly-Supervised Abnormality Localization in Chest X-rays

Creating a large-scale dataset of abnormality annotation on medical images is a labor-intensive and costly task. Leveraging weak supervision from readily available data such as radiology reports can compensate lack of large-scale data for anomaly detection methods. However, most of the current methods only use image-level pathological observations, failing to utilize the relevant anatomy mentions in reports. Furthermore, Natural Language Processing (NLP)-mined weak labels are noisy due to label sparsity and linguistic ambiguity. We propose an Anatomy-Guided chest X-ray Network (AGXNet) to address these issues of weak annotation. Our framework consists of a cascade of two networks, one responsible for identifying anatomical abnormalities and the second responsible for pathological observations. The critical component in our framework is an anatomy-guided attention module that aids the downstream observation network in focusing on the relevant anatomical regions generated by the anatomy network. We use Positive Unlabeled (PU) learning to account for the fact that lack of mention does not necessarily mean a negative label. Our quantitative and qualitative results on the MIMIC-CXR dataset demonstrate the effectiveness of AGXNet in disease and anatomical abnormality localization. Experiments on the NIH Chest X-ray dataset show that the learned feature representations are transferable and can achieve the state-of-the-art performances in disease classification and competitive disease localization results. Our code is available at https://github.com/batmanlab/AGXNet

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Boosting the interpretability of clinical risk scores with intervention predictions

Machine learning systems show significant promise for forecasting patient adverse events via risk scores. However, these risk scores implicitly encode assumptions about future interventions that the patient is likely to receive, based on the intervention policy present in the training data. Without this important context, predictions from such systems are less interpretable for clinicians. We propose a joint model of intervention policy and adverse event risk as a means to explicitly communicate the model's assumptions about future interventions. We develop such an intervention policy model on MIMIC-III, a real world de-identified ICU dataset, and discuss some use cases that highlight the utility of this approach. We show how combining typical risk scores, such as the likelihood of mortality, with future intervention probability scores leads to more interpretable clinical predictions.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Context-aware Self-supervised Learning for Medical Images Using Graph Neural Network

Although self-supervised learning enables us to bootstrap the training by exploiting unlabeled data, the generic self-supervised methods for natural images do not sufficiently incorporate the context. For medical images, a desirable method should be sensitive enough to detect deviation from normal-appearing tissue of each anatomical region; here, anatomy is the context. We introduce a novel approach with two levels of self-supervised representation learning objectives: one on the regional anatomical level and another on the patient-level. We use graph neural networks to incorporate the relationship between different anatomical regions. The structure of the graph is informed by anatomical correspondences between each patient and an anatomical atlas. In addition, the graph representation has the advantage of handling any arbitrarily sized image in full resolution. Experiments on large-scale Computer Tomography (CT) datasets of lung images show that our approach compares favorably to baseline methods that do not account for the context. We use the learned embedding for staging lung tissue abnormalities related to COVID-19.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Hierarchical Amortized Training for Memory-efficient High Resolution 3D GAN

Generative Adversarial Networks (GAN) have many potential medical imaging applications, including data augmentation, domain adaptation, and model explanation. Due to the limited memory of Graphical Processing Units (GPUs), most current 3D GAN models are trained on low-resolution medical images, these models either cannot scale to high-resolution or are prone to patchy artifacts. In this work, we propose a novel end-to-end GAN architecture that can generate high-resolution 3D images. We achieve this goal by using different configurations between training and inference. During training, we adopt a hierarchical structure that simultaneously generates a low-resolution version of the image and a randomly selected sub-volume of the high-resolution image. The hierarchical design has two advantages: First, the memory demand for training on high-resolution images is amortized among sub-volumes. Furthermore, anchoring the high-resolution sub-volumes to a single low-resolution image ensures anatomical consistency between sub-volumes. During inference, our model can directly generate full high-resolution images. We also incorporate an encoder with a similar hierarchical structure into the model to extract features from the images. Experiments on 3D thorax CT and brain MRI demonstrate that our approach outperforms state of the art in image generation. We also demonstrate clinical applications of the proposed model in data augmentation and clinical-relevant feature extraction.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Hyperbolic Molecular Representation Learning for Drug Repositioning

Learning accurate drug representations is essential for task such as computational drug repositioning. A drug hierarchy is a valuable source that encodes knowledge of relations among drugs in a tree-like structure where drugs that act on the same organs, treat the same disease, or bind to the same biological target are grouped together. However, its utility in learning drug representations has not yet been explored, and currently described drug representations cannot place novel molecules in a drug hierarchy. Here, we develop a semi-supervised drug embedding that incorporates two sources of information: (1) underlying chemical grammar that is inferred from chemical structures of drugs and drug-like molecules (unsupervised), and (2) hierarchical relations that are encoded in an expert-crafted hierarchy of approved drugs (supervised). We use the Variational Auto-Encoder (VAE) framework to encode the chemical structures of molecules and use the drug-drug similarity information obtained from the hierarchy to induce the clustering of drugs in hyperbolic space. The hyperbolic space is amenable for encoding hierarchical relations. Our qualitative results support that the learned drug embedding can induce the hierarchical relations among drugs. We demonstrate that the learned drug embedding can be used for drug repositioning.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Imaging and Spectroscopic Observations of the Dynamic Processes in Limb Solar Flares

We investigate various dynamic processes including magnetic reconnection, chromospheric evaporation, and coronal rain draining in two limb solar flares through imaging and spectroscopic observations from the Interface Region Imaging Spectrograph (IRIS) and the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory. In the early phase of the flares, a bright and dense loop-top structure with a cusp-like shape can be seen in multi-wavelength images, which is co-spatial with the hard X-ray 25--50 keV emission. In particular, intermittent magnetic reconnection downflows are detected in the time-space maps of AIA 304 Å. The reconnection downflows are manifested as redshifts on one half of the loops and blueshifts on the other half in the IRIS Si {\sc iv} 1393.76 Å line due to a projection effect. The Si {\sc iv} profiles exhibit complex features (say, multi-peak) with a relatively larger width at the loop-top region. During the impulsive phase, chromospheric evaporation is observed in both AIA images and the IRIS Fe {\sc xxi} 1354.08 Å line. Upward motions can be seen from AIA 131 Å images. The Fe {\sc xxi} line is significantly enhanced and shows a good Gaussian shape. In the gradual phase, warm rains are observed as downward moving plasmas in AIA 304 Å images. Both the Si {\sc iv} and Fe {\sc xxi} lines show a relatively symmetric shape with a larger width around the loop top. These results provide observational evidence for various dynamic processes involved in and are crucial to understand the energy release process of solar flares.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

IRIS Si IV Line Profiles at Flare Ribbons as Indications of Chromospheric Condensation

We present temporal variations of the Si IV line profiles at the flare ribbons in three solar flares observed by the Interface Region Imaging Spectrograph (IRIS). In the M1.1 flare on 2014 September 6 and the X1.6 flare on 2014 September 10, the Si IV line profiles evolve from wholly redshifted to red-wing enhanced with the flare development. However, in the B1.8 flare on 2016 December 2, the Si IV line profiles are wholly redshifted throughout the flare evolution. We fit the wholly redshifted line profiles with a single Gaussian function but the red-asymmetric ones with a double Gaussian function to deduce the corresponding Doppler velocities. In addition, we find that hard X-ray emission above 25 keV shows up in the two large flares, implying a nonthermal electron beam heating. In the microflare, there only appears weak hard X-ray emission up to 12 keV, indicative of a thermal heating mostly. We interpret the redshifts or red asymmetries of the Si IV line at the ribbons in the three flares as spectral manifestations of chromospheric condensation. We propose that whether the line appears to be wholly redshifted or red-asymmetric depends on the heating mechanisms and also on the propagation of the condensation.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Semi-Supervised Hierarchical Drug Embedding in Hyperbolic Space

Learning accurate drug representation is essential for tasks such as computational drug repositioning and prediction of drug side-effects. A drug hierarchy is a valuable source that encodes human knowledge of drug relations in a tree-like structure where drugs that act on the same organs, treat the same disease, or bind to the same biological target are grouped together. However, its utility in learning drug representations has not yet been explored, and currently described drug representations cannot place novel molecules in a drug hierarchy. Here, we develop a semi-supervised drug embedding that incorporates two sources of information: (1) underlying chemical grammar that is inferred from molecular structures of drugs and drug-like molecules (unsupervised), and (2) hierarchical relations that are encoded in an expert-crafted hierarchy of approved drugs (supervised). We use the Variational Auto-Encoder (VAE) framework to encode the chemical structures of molecules and use the knowledge-based drug-drug similarity to induce the clustering of drugs in hyperbolic space. The hyperbolic space is amenable for encoding hierarchical concepts. Both quantitative and qualitative results support that the learned drug embedding can accurately reproduce the chemical structure and induce the hierarchical relations among drugs. Furthermore, our approach can infer the pharmacological properties of novel molecules by retrieving similar drugs from the embedding space. We demonstrate that the learned drug embedding can be used to find new uses for existing drugs and to discover side-effects. We show that it significantly outperforms baselines in both tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Understanding Deformable Alignment in Video Super-Resolution

Deformable convolution, originally proposed for the adaptation to geometric variations of objects, has recently shown compelling performance in aligning multiple frames and is increasingly adopted for video super-resolution. Despite its remarkable performance, its underlying mechanism for alignment remains unclear. In this study, we carefully investigate the relation between deformable alignment and the classic flow-based alignment. We show that deformable convolution can be decomposed into a combination of spatial warping and convolution. This decomposition reveals the commonality of deformable alignment and flow-based alignment in formulation, but with a key difference in their offset diversity. We further demonstrate through experiments that the increased diversity in deformable alignment yields better-aligned features, and hence significantly improves the quality of video super-resolution output. Based on our observations, we propose an offset-fidelity loss that guides the offset learning with optical flow. Experiments show that our loss successfully avoids the overflow of offsets and alleviates the instability problem of deformable alignment. Aside from the contributions to deformable alignment, our formulation inspires a more flexible approach to introduce offset diversity to flow-based alignment, improving its performance.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

A fast multi-resolution lattice Green's function method for elliptic difference equations

We propose a mesh refinement technique for solving elliptic difference equations on unbounded domains based on the fast lattice Green's function (FLGF) method. The FLGF method exploits the regularity of the Cartesian mesh and uses the fast multipole method in conjunction with fast Fourier transforms to yield linear complexity and decrease time-to-solution. We extend this method to a multi-resolution scheme and allow for locally refined Cartesian blocks embedded in the computational domain. Appropriately chosen interpolation and regularization operators retain consistency between the discrete Laplace operator and its inverse on the unbounded domain. Second-order accuracy and linear complexity are maintained, while significantly reducing the number of degrees of freedom and hence the computational cost.

0 citations0 reviewsNo code linkedOpen access