Triplet Loss based Metric Learning for Cervical Cell Classification

Person re-identification (ReID) aims to match people across multiple non-overlapping video cameras deployed at different locations. To address this challenging problem, many metric learning approaches have been proposed, among which triplet loss is one of the state-of-the-arts. In this work, we explore the margin between positive and negative pairs of triplets and prove that large margin is beneficial. In particular, we propose a novel multi-stage training strategy which learns incremental triplet margin and improves triplet loss effectively. Multiple levels of feature maps are exploited to make the learned features more discriminative. Besides, we introduce global hard identity searching method to sample hard identities when generating a training batch. Extensive experiments on Market-1501, CUHK03, and DukeMTMCreID show that our approach yields a performance boost and outperforms most existing state-of-the-art methods.

Download Full-text

A More Comprehensive Cervical Cell Classification Using Convolutional Neural Network

Journal of the American Society of Cytopathology ◽

10.1016/j.jasc.2018.06.156 ◽

2018 ◽

Vol 7 (5) ◽

pp. S66 ◽

Cited By ~ 1

Author(s):

Vanessa Martin ◽

Tae Hun Kim ◽

Melanie Kwon ◽

Mohammed Kuko ◽

Mohammad Pourhomayoun ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Cell Classification ◽

Cervical Cell

Download Full-text

Enhancing Multi-tissue and Multi-scale Cell Nuclei Segmentation with Deep Metric Learning

Applied Sciences ◽

10.3390/app10020615 ◽

2020 ◽

Vol 10 (2) ◽

pp. 615 ◽

Cited By ~ 2

Author(s):

Tomas Iesmantas ◽

Agne Paulauskaite-Taraseviciene ◽

Kristina Sutiene

Keyword(s):

Deep Learning ◽

Large Scale ◽

Metric Learning ◽

Cell Nuclei ◽

Similarity Coefficients ◽

Clinical Practices ◽

Nuclei Segmentation ◽

Wide Range ◽

Triplet Loss ◽

Deep Metric Learning

(1) Background: The segmentation of cell nuclei is an essential task in a wide range of biomedical studies and clinical practices. The full automation of this process remains a challenge due to intra- and internuclear variations across a wide range of tissue morphologies, differences in staining protocols and imaging procedures. (2) Methods: A deep learning model with metric embeddings such as contrastive loss and triplet loss with semi-hard negative mining is proposed in order to accurately segment cell nuclei in a diverse set of microscopy images. The effectiveness of the proposed model was tested on a large-scale multi-tissue collection of microscopy image sets. (3) Results: The use of deep metric learning increased the overall segmentation prediction by 3.12% in the average value of Dice similarity coefficients as compared to no metric learning. In particular, the largest gain was observed for segmenting cell nuclei in H&E -stained images when deep learning network and triplet loss with semi-hard negative mining were considered for the task. (4) Conclusion: We conclude that deep metric learning gives an additional boost to the overall learning process and consequently improves the segmentation performance. Notably, the improvement ranges approximately between 0.13% and 22.31% for different types of images in the terms of Dice coefficients when compared to no metric deep learning.

Download Full-text

Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/403 ◽

2019 ◽

Author(s):

Pengcheng Li ◽

Jinfeng Yi ◽

Bowen Zhou ◽

Lijun Zhang

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Current Model ◽

Empirical Studies ◽

Metric Learning ◽

Distance Metric Learning ◽

Distance Metric ◽

Classification Boundary ◽

Adversarial Training ◽

Triplet Loss

Recent studies have highlighted that deep neural networks (DNNs) are vulnerable to adversarial examples. In this paper, we improve the robustness of DNNs by utilizing techniques of Distance Metric Learning. Specifically, we incorporate Triplet Loss, one of the most popular Distance Metric Learning methods, into the framework of adversarial training. Our proposed algorithm, Adversarial Training with Triplet Loss (AT2L), substitutes the adversarial example against the current model for the anchor of triplet loss to effectively smooth the classification boundary. Furthermore, we propose an ensemble version of AT2L, which aggregates different attack methods and model structures for better defense effects. Our empirical studies verify that the proposed approach can significantly improve the robustness of DNNs without sacrificing accuracy. Finally, we demonstrate that our specially designed triplet loss can also be used as a regularization term to enhance other defense methods.

Download Full-text