Author name cluster

Ao Xu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

JBHI Journal 2026 Journal Article

NoTAC: A Noise-Tolerance Automatic Cleaning Framework for Bone Marrow Karyotyping Data

Rihan Huang
Siyuan Chen
Yafei Li
Chunling Zhang
Yilan Zhang
Changchun Yang
Na Li
Jingdong Hu

Deep neural networks have advanced chromosome classification, a critical procedure in karyotyping for disease diagnosis. However, training an effective DNN requires clean and reliable data, whereas real-world clinical chromosome data often contain label errors and outliers, which degrade DNN performance and limit their clinical applicability. In this work, we propose a Noise-Tolerance Automatic Cleaning framework, named NoTAC, to address potential labeling errors and outliers to enhance the performance of chromosome classification. The framework consists of two branches: KaryoCleanse for label noise detection and KaryoDrift for outlier identification. First, it identifies potential label errors by leveraging the DNN’s self-confidence, estimating the latent label distribution, and ranking probabilities to prune mislabeled data. Second, it scores out-of-distribution samples based on the average K-nearest neighbor distances, enabling the identification and removal of outlier data. We conducted comprehensive comparative experiments against state-of-the-art noise-handling methods on a real-world R-band bone marrow chromosome dataset. Our results demonstrate that NoTAC achieves superior performance with an accuracy of 93. 99%, which represents a 6. 25% relative improvement over the baseline and outperforms the best competing method by 0. 92%. Furthermore, our qualitative analysis of NoTAC revealed reliable data issues in a real-world R-band bone marrow chromosome dataset, offering insights into how these issues impair DNN prediction capabilities. These findings demonstrate NoTAC’s potential to enhance both the performance and reliability of DNNs in practical medical datasets. The proposed method has also been applied to assist clinical karyotype diagnosis.

Details DOI

TMLR Journal 2023 Journal Article

Invariant Structure Learning for Better Generalization and Causal Explainability

Yunhao Ge
Sercan O Arik
Jinsung Yoon
Ao Xu
Laurent Itti
Tomas Pfister

Learning the causal structure behind data is invaluable for improving generalization and ob- taining high-quality explanations. Towards this end, we propose a novel framework, Invariant Structure Learning (ISL), that is designed to improve causal structure discovery by utilizing generalization as an indication in the process. ISL splits the data into different environments, and learns a structure that is invariant to the target across different environments by imposing a consistency constraint. The proposed aggregation mechanism then selects the classifier based on a graph structure that reflects the causal mechanisms in the data more accurately compared to the structures learnt from individual environments. Furthermore, we extend ISL to a self-supervised learning setting, where accurate causal structure discovery does not rely on any labels. Self-supervised ISL utilizes proposals for invariant causality, by iteratively setting different nodes as targets. On synthetic and real-world datasets, we demonstrate that ISL accurately discovers the causal structure, outperforms alternative methods, and yields superior generalization for datasets with significant distribution shifts.

PDF Details

TMLR Journal 2023 Journal Article

Lightweight Learner for Shared Knowledge Lifelong Learning

Yunhao Ge
Yuecheng Li
Di Wu
Ao Xu
Adam M. Jones
Amanda Sofie Rios
Iordanis Fostiropoulos
shixian wen

In Lifelong Learning (LL), agents continually learn as they encounter new conditions and tasks. Most current LL is limited to a single agent that learns tasks sequentially. Dedicated LL machinery is then deployed to mitigate the forgetting of old tasks as new tasks are learned. This is inherently slow. We propose a new Shared Knowledge Lifelong Learning (SKILL) challenge, which deploys a decentralized population of LL agents that each sequentially learn different tasks, with all agents operating independently and in parallel. After learning their respective tasks, agents share and consolidate their knowledge over a decentralized communication network, so that, in the end, all agents can master all tasks. We present one solution to SKILL which uses Lightweight Lifelong Learning (LLL) agents, where the goal is to facilitate efficient sharing by minimizing the fraction of the agent that is specialized for any given task. Each LLL agent thus consists of a common task-agnostic immutable part, where most parameters are, and individual task-specific modules that contain fewer parameters but are adapted to each task. Agents share their task-specific modules, plus summary information ("task anchors") representing their tasks in the common task-agnostic latent space of all agents. Receiving agents register each received task-specific module using the corresponding anchor. Thus, every agent improves its ability to solve new tasks each time new task-specific modules and anchors are received. If all agents can communicate with all others, eventually all agents become identical and can solve all tasks. On a new, very challenging SKILL-102 dataset with 102 image classification tasks (5,033 classes in total, 2,041,225 training, 243,464 validation, and 243,464 test images), we achieve much higher (and SOTA) accuracy over 8 LL baselines, while also achieving near perfect parallelization. Code and data can be found at https://github.com/gyhandy/Shared-Knowledge-Lifelong-Learning

PDF Details

JBHI Journal 2020 Journal Article

Deep Learning-Based Classification of Liver Cancer Histopathology Images Using Only Global Labels

Chunli Sun
Ao Xu
Dong Liu
Zhiwei Xiong
Feng Zhao
Weiping Ding

Liver cancer is a leading cause of cancer deaths worldwide due to its high morbidity and mortality. Histopathological image analysis (HIA) is a crucial step in the early diagnosis of liver cancer and is routinely performed manually. However, this process is time-consuming, error-prone, and easily affected by the expertise of pathologists. Recently, computer-aided methods have been widely applied to medical image analysis; however, the current medical image analysis studies have not yet focused on the histopathological morphology of liver cancer due to its complex features and the insufficiency of training images with detailed annotations. This paper proposes a deep learning method for liver cancer histopathological image classification using only global labels. To compensate for the lack of detailed cancer region annotations in those images, patch features are extracted and fully utilized. Transfer learning is used to obtain the patch-level features and then combined with multiple-instance learning to acquire the image-level features for classification. The method proposed here solves the processing of large-scale images and training sample insufficiency in liver cancer histopathological images for image classification. The proposed method can distinguish and classify liver histopathological images as abnormal or normal with high accuracy, thus providing support for the early diagnosis of liver cancer.

Details DOI