# Domain Adaptation-based Augmentation for Weakly Supervised Nuclei Detection

arXiv: https://arxiv.org/abs/1907.04681 · HTML: https://mlsanity.com/paper/1907.04681/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2019.

## Built on by (most cited first, 9 of 9)

- [A comparative evaluation of image-to-image translation methods for stain transfer in histopathology](https://mlsanity.com/paper/2303.17009/) (2023)
- [Fluorescent Neuronal Cells v2: Multi-Task, Multi-Format Annotations for Deep Learning in Microscopy](https://mlsanity.com/paper/2307.14243/) (2023)
- [AD-GAN: End-to-end Unsupervised Nuclei Segmentation with Aligned Disentangling Training](https://mlsanity.com/paper/2107.11022/) (2021)
- [Primary Tumor and Inter-Organ Augmentations for Supervised Lymph Node Colon Adenocarcinoma Metastasis Detection](https://mlsanity.com/paper/2109.09518/) (2021)
- [Stain Isolation-based Guidance for Improved Stain Translation](https://mlsanity.com/paper/2207.00431/) (2022)
- [ReStainGAN: Leveraging IHC to IF Stain Domain Translation for in-silico Data Generation](https://mlsanity.com/paper/2403.06545/) (2024)
- [Auxiliary CycleGAN-guidance for Task-Aware Domain Translation from Duplex to Monoplex IHC Images](https://mlsanity.com/paper/2403.07389/) (2024)
- [Mask-guided cross-image attention for zero-shot in-silico histopathologic image generation with a diffusion model](https://mlsanity.com/paper/2407.11664/) (2024)
- [CyclePose -- Leveraging Cycle-Consistency for Annotation-Free Nuclei Segmentation in Fluorescence Microscopy](https://mlsanity.com/paper/2503.11266/) (2025)

## Abstract (arXiv)

The detection of nuclei is one of the most fundamental components of computational pathology. Current state-of-the-art methods are based on deep learning, with the prerequisite that extensive labeled datasets are available. The increasing number of patient cohorts to be analyzed, the diversity of tissue stains and indications, as well as the cost of dataset labeling motivates the development of novel methods to reduce labeling effort across domains. We introduce in this work a weakly supervised 'inter-domain' approach that (i) performs stain normalization and unpaired image-to-image translation to transform labeled images on a source domain to synthetic labeled images on an unlabeled target domain and (ii) uses the resulting synthetic labeled images to train a detection network on the target domain. Extensive experiments show the superiority of the proposed approach against the state-of-the-art 'intra-domain' detection based on fully-supervised learning.