arXiv subjects

Zhipeng Deng

Publications and source records attributed to Zhipeng Deng.

21 records · Page 2Linked to original sources

arXiv2024

Solving Moving Sofa Problem Using Calculus of Variations

In 1966, Leo Moser introduced the "moving sofa problem," which seeks to determine the largest area of a shape that can be maneuvered through a 90-degree hallway of unit-width. This problem remains unsolved and open yet. In this paper, we employ calculus of variations method to solve this problem. Assuming the trajectories and envelopes are convex, the sofa's area is formulated as an integral functional on a set of parametric equations for curves. The final shape is determined by solving the Euler-Lagrange equations. Utilizing numerical methods, we obtain the non-trivial area of 2.2195316, consistent with the previously well-known Gerver's constant since 1992. We prove that both the results of Gerver's sofa and Romik's car satisfy the Euler-Lagrange equations for the necessary condition of maximal area. We also explore additional cases and asymmetric conditions, and discuss other variant problems.

math.CA↗

arXiv2024

Federated Active Learning Framework for Efficient Annotation Strategy in Skin-lesion Classification

Federated Learning (FL) enables multiple institutes to train models collaboratively without sharing private data. Current FL research focuses on communication efficiency, privacy protection, and personalization and assumes that the data of FL have already been ideally collected. In medical scenarios, however, data annotation demands both expertise and intensive labor, which is a critical problem in FL. Active learning (AL), has shown promising performance in reducing the number of data annotations in medical image analysis. We propose a federated AL (FedAL) framework in which AL is executed periodically and interactively under FL. We exploit a local model in each hospital and a global model acquired from FL to construct an ensemble. We use ensemble-entropy-based AL as an efficient data-annotation strategy in FL. Therefore, our FedAL framework can decrease the amount of annotated data and preserve patient privacy while maintaining the performance of FL. To our knowledge, this is the first FedAL framework applied to medical images. We validated our framework on real-world dermoscopic datasets. Using only 50% of samples, our framework was able to achieve state-of-the-art performance on a skin-lesion classification task. Our framework performed better than several state-of-the-art AL methods under FL and achieved comparable performance to full-data FL.

cs.CV↗

arXiv2023

Scale Federated Learning for Label Set Mismatch in Medical Image Classification

Federated learning (FL) has been introduced to the healthcare domain as a decentralized learning paradigm that allows multiple parties to train a model collaboratively without privacy leakage. However, most previous studies have assumed that every client holds an identical label set. In reality, medical specialists tend to annotate only diseases within their area of expertise or interest. This implies that label sets in each client can be different and even disjoint. In this paper, we propose the framework FedLSM to solve the problem of Label Set Mismatch. FedLSM adopts different training strategies on data with different uncertainty levels to efficiently utilize unlabeled or partially labeled data as well as class-wise adaptive aggregation in the classification layer to avoid inaccurate aggregation when clients have missing labels. We evaluated FedLSM on two public real-world medical image datasets, including chest X-ray (CXR) diagnosis with 112,120 CXR images and skin lesion diagnosis with 10,015 dermoscopy images, and showed that it significantly outperformed other state-of-the-art FL algorithms. The code can be found at https://github.com/dzp2095/FedLSM.

cs.CV↗