# Joint Triplet Autoencoder for Histopathological Colon Cancer Nuclei Retrieval

arXiv: https://arxiv.org/abs/2105.10262 · HTML: https://mlsanity.com/paper/2105.10262/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 88 references that are in the map.
- First posted on arXiv 21 May 2021.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-rays](https://mlsanity.com/paper/1801.04334/) (2018)
- [HashNet: Deep Learning to Hash by Continuation](https://mlsanity.com/paper/1702.00758/) (2017)
- [HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification](https://mlsanity.com/paper/1902.06701/) (2019)
- [A Decade Survey of Content Based Image Retrieval using Deep Learning](https://mlsanity.com/paper/2012.00641/) (2020)
- [Deep Supervised Discrete Hashing](https://mlsanity.com/paper/1705.10999/) (2017)
- [Asymmetric Deep Supervised Hashing](https://mlsanity.com/paper/1707.08325/) (2017)
- [An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection](https://mlsanity.com/paper/1810.10850/) (2018)
- [diffGrad: An Optimization Method for Convolutional Neural Networks](https://mlsanity.com/paper/1909.11015/) (2019)
- [Deep Supervised Hashing with Triplet Labels](https://mlsanity.com/paper/1612.03900/) (2016)
- [Spontaneous Facial Micro-Expression Recognition using 3D Spatiotemporal Convolutional Neural Networks](https://mlsanity.com/paper/1904.01390/) (2019)
- [RCCNet: An Efficient Convolutional Neural Network for Histological Routine Colon Cancer Nuclei Classification](https://mlsanity.com/paper/1810.02797/) (2018)
- [A Performance Evaluation of Convolutional Neural Networks for Face Anti Spoofing](https://mlsanity.com/paper/1805.04176/) (2018)
- [CSGAN: Cyclic-Synthesized Generative Adversarial Networks for Image-to-Image Transformation](https://mlsanity.com/paper/1901.03554/) (2019)
- [Deep Learning Representation using Autoencoder for 3D Shape Retrieval](https://mlsanity.com/paper/1409.7164/) (2014)
- [Average Biased ReLU Based CNN Descriptor for Improved Face Retrieval](https://mlsanity.com/paper/1804.02051/) (2018)
- [SSDH: Semi-supervised Deep Hashing for Large Scale Image Retrieval](https://mlsanity.com/paper/1607.08477/) (2016)
- [Hard-Mining Loss based Convolutional Neural Network for Face Recognition](https://mlsanity.com/paper/1908.09747/) (2019)
- [CDGAN: Cyclic Discriminative Generative Adversarial Networks for Image-to-Image Transformation](https://mlsanity.com/paper/2001.05489/) (2020)

## Built on by (most cited first, 1 of 1)

- [Siamese Content-based Search Engine for a More Transparent Skin and Breast Cancer Diagnosis through Histological Imaging](https://mlsanity.com/paper/2401.08272/) (2024)

## Abstract (arXiv)

Deep learning has shown a great improvement in the performance of visual tasks. Image retrieval is the task of extracting the visually similar images from a database for a query image. The feature matching is performed to rank the images. Various hand-designed features have been derived in past to represent the images. Nowadays, the power of deep learning is being utilized for automatic feature learning from data in the field of biomedical image analysis. Autoencoder and Siamese networks are two deep learning models to learn the latent space (i.e., features or embedding). Autoencoder works based on the reconstruction of the image from latent space. Siamese network utilizes the triplets to learn the intra-class similarity and inter-class dissimilarity. Moreover, Autoencoder is unsupervised, whereas Siamese network is supervised. We propose a Joint Triplet Autoencoder Network (JTANet) by facilitating the triplet learning in autoencoder framework. A joint supervised learning for Siamese network and unsupervised learning for Autoencoder is performed. Moreover, the Encoder network of Autoencoder is shared with Siamese network and referred as the Siamcoder network. The features are extracted by using the trained Siamcoder network for retrieval purpose. The experiments are performed over Histopathological Routine Colon Cancer dataset. We have observed the promising performance using the proposed JTANet model against the Autoencoder and Siamese models for colon cancer nuclei retrieval in histopathological images.