# Robust Interactive Semantic Segmentation of Pathology Images with Minimal User Input arXiv: https://arxiv.org/abs/2108.13368 ยท HTML: https://mlsanity.com/paper/2108.13368/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Builds on 26 of its 40 references that are in the map. - First posted on arXiv 30 Aug 2021. ## Builds on (most cited first, 26 of 26) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015) - [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019) - [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018) - [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017) - [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017) - [Searching for Activation Functions](https://mlsanity.com/paper/1710.05941/) (2017) - [A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1910.02923/) (2019) - [Deep Extreme Cut: From Extreme Points to Object Segmentation](https://mlsanity.com/paper/1711.09081/) (2017) - [PanNuke Dataset Extension, Insights and Baselines](https://mlsanity.com/paper/2003.10778/) (2020) - [Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning](https://mlsanity.com/paper/1710.04043/) (2017) - [Fast Interactive Object Annotation with Curve-GCN](https://mlsanity.com/paper/1903.06874/) (2019) - [Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotations](https://mlsanity.com/paper/2008.05571/) (2020) - [Annotating Object Instances with a Polygon-RNN](https://mlsanity.com/paper/1704.05548/) (2017) - [MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images](https://mlsanity.com/paper/1806.01963/) (2018) - [Methods for Segmentation and Classification of Digital Microscopy Tissue Images](https://mlsanity.com/paper/1810.13230/) (2018) - [NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Images](https://mlsanity.com/paper/2005.14511/) (2020) - [Deep GrabCut for Object Selection](https://mlsanity.com/paper/1707.00243/) (2017) - [Semantic annotation for computational pathology: Multidisciplinary experience and best practice recommendations](https://mlsanity.com/paper/2106.13689/) (2021) - [Nuclear Instance Segmentation using a Proposal-Free Spatially Aware Deep Learning Framework](https://mlsanity.com/paper/1908.10356/) (2019) - [Segmentation of Skin Lesions and their Attributes Using Multi-Scale Convolutional Neural Networks and Domain Specific Augmentations](https://mlsanity.com/paper/1809.10243/) (2018) - [Nuclei Detection Using Mixture Density Networks](https://mlsanity.com/paper/1808.08279/) (2018) - [NuClick: From Clicks in the Nuclei to Nuclear Boundaries](https://mlsanity.com/paper/1909.03253/) (2019) - [Cells are Actors: Social Network Analysis with Classical ML for SOTA Histology Image Classification](https://mlsanity.com/paper/2106.15299/) (2021) - [Automatic Recognition of the Supraspinatus Tendinopathy from Ultrasound Images using Convolutional Neural Networks](https://mlsanity.com/paper/2011.11777/) (2020) ## Built on by (most cited first, 8 of 8) - [Mitosis domain generalization in histopathology images -- The MIDOG challenge](https://mlsanity.com/paper/2204.03742/) (2022) - [WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma](https://mlsanity.com/paper/2204.06455/) (2022) - [Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy](https://mlsanity.com/paper/2311.13964/) (2023) - [Domain generalization across tumor types, laboratories, and species -- insights from the 2022 edition of the Mitosis Domain Generalization Challenge](https://mlsanity.com/paper/2309.15589/) (2023) - [Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge](https://mlsanity.com/paper/2109.00853/) (2021) - [TIAger: Tumor-Infiltrating Lymphocyte Scoring in Breast Cancer for the TiGER Challenge](https://mlsanity.com/paper/2206.11943/) (2022) - [Consistency Regularisation in Varying Contexts and Feature Perturbations for Semi-Supervised Semantic Segmentation of Histology Images](https://mlsanity.com/paper/2301.13141/) (2023) - [Benchmarking Domain Generalization Algorithms in Computational Pathology](https://mlsanity.com/paper/2409.17063/) (2024) ## Abstract (arXiv) From the simple measurement of tissue attributes in pathology workflow to designing an explainable diagnostic/prognostic AI tool, access to accurate semantic segmentation of tissue regions in histology images is a prerequisite. However, delineating different tissue regions manually is a laborious, time-consuming and costly task that requires expert knowledge. On the other hand, the state-of-the-art automatic deep learning models for semantic segmentation require lots of annotated training data and there are only a limited number of tissue region annotated images publicly available. To obviate this issue in computational pathology projects and collect large-scale region annotations efficiently, we propose an efficient interactive segmentation network that requires minimum input from the user to accurately annotate different tissue types in the histology image. The user is only required to draw a simple squiggle inside each region of interest so it will be used as the guiding signal for the model. To deal with the complex appearance and amorph geometry of different tissue regions we introduce several automatic and minimalistic guiding signal generation techniques that help the model to become robust against the variation in the user input. By experimenting on a dataset of breast cancer images, we show that not only does our proposed method speed up the interactive annotation process, it can also outperform the existing automatic and interactive region segmentation models.