Dezhan Tu

Dezhan Tu, Yeye He, Weiwei Cui et al.

Data pipelines are widely employed in modern enterprises to power a variety of Machine-Learning (ML) and Business-Intelligence (BI) applications. Crucially, these pipelines are \emph{recurring} (e.g., daily or hourly) in production settings to keep data updated so that ML models can be re-trained regularly, and BI dashboards refreshed frequently. However, data quality (DQ) issues can often creep into recurring pipelines because of upstream schema and data drift over time. As modern enterprises operate thousands of recurring pipelines, today data engineers have to spend substantial efforts to \emph{manually} monitor and resolve DQ issues, as part of their DataOps and MLOps practices. Given the high human cost of managing large-scale pipeline operations, it is imperative that we can \emph{automate} as much as possible. In this work, we propose Auto-Validate-by-History (AVH) that can automatically detect DQ issues in recurring pipelines, leveraging rich statistics from historical executions. We formalize this as an optimization problem, and develop constant-factor approximation algorithms with provable precision guarantees. Extensive evaluations using 2000 production data pipelines at Microsoft demonstrate the effectiveness and efficiency of AVH.

Weinan Song, Haoxin Zheng, Dezhan Tu et al.

3D reconstruction of medical imaging from 2D images has become an increasingly interesting topic with the development of deep learning models in recent years. Previous studies in 3D reconstruction from limited X-ray images mainly rely on learning from paired 2D and 3D images, where the reconstruction quality relies on the scale and variation of collected data. This has brought significant challenges in the collection of training data, as only a tiny fraction of patients take two types of radiation examinations in the same period. Although simulation from higher-dimension images could solve this problem, the variance between real and simulated data could bring great uncertainty at the same time. In oral reconstruction, the situation becomes more challenging as only a single panoramic X-ray image is available, where models need to infer the curved shape by prior individual knowledge. To overcome these limitations, we propose Oral-3Dv2 to solve this cross-dimension translation problem in dental healthcare by learning solely on projection information, i.e., the projection image and trajectory of the X-ray tube. Our model learns to represent the 3D oral structure in an implicit way by mapping 2D coordinates into density values of voxels in the 3D space. To improve efficiency and effectiveness, we utilize a multi-head model that predicts a bunch of voxel values in 3D space simultaneously from a 2D coordinate in the axial plane and the dynamic sampling strategy to refine details of the density distribution in the reconstruction result. Extensive experiments in simulated and real data show that our model significantly outperforms existing state-of-the-art models without learning from paired images or prior individual knowledge. To the best of our knowledge, this is the first work of a non-adversarial-learning-based model in 3D radiology reconstruction from a single panoramic X-ray image.

Dezhan Tu, Danylo Vashchilenko, Yuzhe Lu et al.

Vision-Language Models (VLMs) have demonstrated impressive performance across a versatile set of tasks. A key challenge in accelerating VLMs is storing and accessing the large Key-Value (KV) cache that encodes long visual contexts, such as images or videos. While existing KV cache compression methods are effective for Large Language Models (LLMs), directly migrating them to VLMs yields suboptimal accuracy and speedup. To bridge the gap, we propose VL-Cache, a novel KV cache compression recipe tailored for accelerating VLM inference. In this paper, we first investigate the unique sparsity pattern of VLM attention by distinguishing visual and text tokens in prefill and decoding phases. Based on these observations, we introduce a layer-adaptive sparsity-aware cache budget allocation method that effectively distributes the limited cache budget across different layers, further reducing KV cache size without compromising accuracy. Additionally, we develop a modality-aware token scoring policy to better evaluate the token importance. Empirical results on multiple benchmark datasets demonstrate that retaining only 10% of KV cache achieves accuracy comparable to that with full cache. In a speed benchmark, our method accelerates end-to-end latency of generating 100 tokens by up to 2.33x and speeds up decoding by up to 7.08x, while reducing the memory footprint of KV cache in GPU by 90%.

Yuan Liang, Liang Qiu, Tiancheng Lu et al.

Patient's understanding on forthcoming dental surgeries is required by patient-centered care and helps reduce fear and anxiety. Due to the gap of expertise between patients and dentists, conventional techniques of patient education are usually not effective for explaining surgical steps. In this paper, we present \textit{OralViewer} -- the first interactive application that enables dentist's demonstration of dental surgeries in 3D to promote patients' understanding. \textit{OralViewer} takes a single 2D panoramic dental X-ray to reconstruct patient-specific 3D teeth structures, which are then assembled with registered gum and jaw bone models for complete oral cavity modeling. During the demonstration, \textit{OralViewer} enables dentists to show surgery steps with virtual dental instruments that can animate effects on a 3D model in real-time. A technical evaluation shows our deep learning based model achieves a mean Intersection over Union (IoU) of 0.771 for 3D teeth reconstruction. A patient study with 12 participants shows \textit{OralViewer} can improve patients' understanding of surgeries. An expert study with 3 board-certified dentists further verifies the clinical validity of our system.