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Cytology inspection is a basic examination for the prevention of cervical cancer, which is still done manually and is a labor-intensive and time-consuming process with high inter-observer variability. With the rapid development of deep learning, automatic cytology inspection methods have achieved gratifying results, but the research on the effective detection of abnormal cells remains insufficient. In this paper, we propose a novel Dual-path Proposal Discriminative detection Network (DPD-Net) for abnormal cell detection in cervical cytology images. Specifically, considering the distinctive characteristics of abnormal nuclei such as increased sizes and unclear boundaries, we first design a dual-path architecture, where the cell path acts as the primary detector and the nucleus path serves as the auxiliary detector to provide supplementary information. In addition, the proposal information isolation may result in ambiguous classification boundaries. To transcend individual features and delve deeper into cellular relationships as implicit features, the proposal relation modules are added to explore relation information and refine features adaptively. Finally, to tackle problems of intra-class variation and inter-class similarity, we calculate the proposal contrastive losses in two paths respectively to guide a better distinction between the features of normal and abnormal cells. The proposed method is evaluated on our in-house cervical liquid-based cytology dataset (CLBC) and a public pap smear cytology dataset (CRIC) and gains satisfactory results, outperforming other state-of-the-art methods. Comprehensive experiments demonstrate the superiority of the proposed DPD-Net and the effectiveness of each proposed component. Our code is available at https://github.com/chaisiyii/DPD-Net. • We design a dual-path network (DP) to parallelly detect abnormal cells and nuclei. • We add proposal relation modules (PRM) to two paths to refine features adaptively. • We add the proposal contrastive loss (PCL) to lead a discriminative feature learning. • We conduct intensive experiments on two cervical cytology datasets: CLBC, CRIC. [Display omitted] [ABSTRACT FROM AUTHOR]

Large-scale image classification is a challenging task and has recently attracted active research interests. In this paper, a new algorithm is developed to achieve more effective implementation of large-scale image classification by hierarchical learning of large-margin metrics (HLMMs). A hierarchical visual tree is seamlessly integrated with metric learning to learn a set of node-specific/category-specific large-margin metrics. First, a hierarchical visual tree is learned to characterize the inter-category visual correlations effectively and organize large numbers of image categories in a coarse-to-fine fashion. Second, a new algorithm is developed to support hierarchical learning of large-margin metrics by training nearest class mean (NCM) classifiers over our hierarchical visual tree. In addition, we also consider dimensionality reduction as a regularizer for high-dimensional data in our large-margin metric learning. Two top-down approaches are developed for supporting hierarchical learning of large-margin metrics. We focus on learning more discriminative metrics for NCM node classifiers to identify the visually similar sub-nodes (visually similar image categories) under the same parent node over our hierarchical visual tree. A mini-batch stochastic gradient descend method is used to optimize our HLMMs learning algorithm. The experimental results on ImageNet Large Scale Visual Recognition Challenge 2010 dataset (ILSVRC2010) have demonstrated that our HLMMs learning algorithm is very promising for supporting large-scale image classification. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel method to address the challenge of learning deep neural network models in the presence of open-set noisy labels, which include mislabeled samples from out-of-distribution categories. Previous methods relied on the distances between sample-wise predictions and labels to identify mislabeled samples and distinguish between in-distribution (ID) and out-of-distribution (OOD) noisy samples, which struggle to promptly identify the two types of noisy samples. To overcome these limitations, we propose a novel method that utilizes feature information and cross-instance relationships, enabling a more comprehensive distinction between ID and OOD noisy samples. Our approach involves a multi-prototype modeling mechanism, where each class is represented by multiple prototypes to account for the diversity within categories. This mechanism helps in distinguishing in-distribution and out-of-distribution noisy samples by comparing sample features with class prototypes. We introduce an online algorithm for updating prototypes and enhancing model optimization with cross-augmentation consistency and a noise-robust contrastive siamese learning technique. Our extensive experiments on datasets like CIFAR100, Clothing1M, and Food101N show our method's superiority in handling noisy labels compared to existing approaches. The code will be available at https://github.com/daisyarg/LNL-MPM. • Propose a multi-prototype modeling mechanism for learning with noisy labels. • Use cross-augmentation and siamese losses for training. • Outperform others in tests on CIFAR100, Clothing1M and Food101N. [ABSTRACT FROM AUTHOR]

In this paper, a robust echo state network with correntropy induced loss function (CLF) is presented. CLF is robust to outliers through the mechanism of correntropy which is widely applied in information theoretic learning. The proposed method can improve the anti-noise capacity of echo state network and overcome its problem of being sensitive outliers which are prevalent in real-world tasks. The echo state network with CLF inherits the basic architecture of echo state network, but replaces the commonly used mean square error (MSE) criterion with CLF. The stochastic gradient descent method is adopted to optimize the objective function. The proposed method is subsequently verified in nonlinear system identification and chaotic time-series prediction. Experimental results demonstrate that our method is robust to outliers and outperforms the echo state networks with Bayesian regression and Huber loss function. [ABSTRACT FROM AUTHOR]