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Zhe Dong

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5 papers

2 author rows

JBHI Journal 2026 Journal Article

Refocal Loss in Transformer for Long-Tailed Multi-Granularity Cataract Classification

Qiong Wang
Yan Wang
Hongdi Sun
Yu Feng
Zhe Dong
Cong Bai

Different cataract types and various severities usually require different countermeasures. For automatic cataract diagnosis, existing cataract classification methods group cataracts into common types, such as nuclear cataract, cortical cataract, and posterior subcapsular cataract, while existing cataract grading works aim to achieve fine-grained evaluation of the severity of the most common types of cataract. The severity assessment differs among various types of cataracts. Existing work is limited in predicting various cataract types at different granularity levels. In order to improve diagnostic efficiency, our study explores this matter in the context of multi-granularity cataract classification. Firstly, a large-scale dataset called Multi-Granularity Long-Tailed Cataract is collected. Secondly, an end-to-end training network is proposed, in which the Transformer is investigated for the extraction of multi-granularity cataract features. What is more, considering the imbalanced cataract data with the long-tailed distribution, the Refocal loss is proposed to rebalance the loss contribution of different classes by enhancing the reciprocal value of the effective number of samples. Compared with state-of-the-art methods, the experiments conducted on the multi-granularity cataract classification dataset demonstrate that the proposed model achieves the highest Precision of 78. 22%, F1-score of 68. 35%, Kappa of 64. 38% and MCC of 64. 49%, indicating that the proposed framework is promising in offering physicians reliable quantitative evaluations for multi-granularity cataract classification, which can help guide appropriate treatment decisions before the patient’s cataracts worsen.

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NeurIPS Conference 2021 Conference Paper

Coupled Gradient Estimators for Discrete Latent Variables

Zhe Dong
Andriy Mnih
George Tucker

Training models with discrete latent variables is challenging due to the high variance of unbiased gradient estimators. While low-variance reparameterization gradients of a continuous relaxation can provide an effective solution, a continuous relaxation is not always available or tractable. Dong et al. (2020) and Yin et al. (2020) introduced a performant estimator that does not rely on continuous relaxations; however, it is limited to binary random variables. We introduce a novel derivation of their estimator based on importance sampling and statistical couplings, which we extend to the categorical setting. Motivated by the construction of a stick-breaking coupling, we introduce gradient estimators based on reparameterizing categorical variables as sequences of binary variables and Rao-Blackwellization. In systematic experiments, we show that our proposed categorical gradient estimators provide state-of-the-art performance, whereas even with additional Rao-Blackwellization previous estimators (Yin et al. , 2019) underperform a simpler REINFORCE with a leave-one-out-baseline estimator (Kool et al. , 2019).

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ICML Conference 2020 Conference Paper

Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems

Zhe Dong
Bryan A. Seybold
Kevin Murphy 0002
Hung H. Bui

We propose an efficient inference method for switching nonlinear dynamical systems. The key idea is to learn an inference network which can be used as a proposal distribution for the continuous latent variables, while performing exact marginalization of the discrete latent variables. This allows us to use the reparameterization trick, and apply end-to-end training with stochastic gradient descent. We show that the proposed method can successfully segment time series data, including videos and 3D human pose, into meaningful “regimes” by using the piece-wise nonlinear dynamics.

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NeurIPS Conference 2020 Conference Paper

DisARM: An Antithetic Gradient Estimator for Binary Latent Variables

Zhe Dong
Andriy Mnih
George Tucker

Training models with discrete latent variables is challenging due to the difficulty of estimating the gradients accurately. Much of the recent progress has been achieved by taking advantage of continuous relaxations of the system, which are not always available or even possible. The Augment-REINFORCE-Merge (ARM) estimator provides an alternative that, instead of relaxation, uses continuous augmentation. Applying antithetic sampling over the augmenting variables yields a relatively low-variance and unbiased estimator applicable to any model with binary latent variables. However, while antithetic sampling reduces variance, the augmentation process increases variance. We show that ARM can be improved by analytically integrating out the randomness introduced by the augmentation process, guaranteeing substantial variance reduction. Our estimator, DisARM, is simple to implement and has the same computational cost as ARM. We evaluate DisARM on several generative modeling benchmarks and show that it consistently outperforms ARM and a strong independent sample baseline in terms of both variance and log-likelihood. Furthermore, we propose a local version of DisARM designed for optimizing the multi-sample variational bound, and show that it outperforms VIMCO, the current state-of-the-art method.

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SODA Conference 2017 Conference Paper

Parameter-free Topology Inference and Sparsification for Data on Manifolds

Tamal K. Dey
Zhe Dong
Yusu Wang 0001

In topology inference from data, current approaches face two major problems. One concerns the selection of a correct parameter to build an appropriate complex on top of the data points; the other involves with the typical ‘large’ size of this complex. We address these two issues in the context of inferring homology from sample points of a smooth manifold of known dimension sitting in an Euclidean space ℝ k. We show that, for a sample size of n points, we can identify a set of O ( n 2 ) points (as opposed to Voronoi vertices) approximating a subset of the medial axis that suffices to compute a distance sandwiched between the well known local feature size and the local weak feature size (in fact, the approximating set can be further reduced in size to O ( n )). This distance, called the lean feature size, helps pruning the input set at least to the level of local feature size while making the data locally uniform. The local uniformity in turn helps in building a complex for homology inference on top of the sparsified data without requiring any user-supplied distance threshold. Unlike most topology inference results, ours does not require that the input is dense relative to a global feature such as reach or weak feature size; instead it can be adaptive with respect to the local feature size. We present some empirical evidence in support of our theoretical claims.

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