# Iterative annotation to ease neural network training: Specialized machine learning in medical image analysis

arXiv: https://arxiv.org/abs/1812.07509 · HTML: https://mlsanity.com/paper/1812.07509/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2018.

## Built on by (most cited first, 21 of 21)

- [Few-shot Medical Image Segmentation with Cycle-resemblance Attention](https://mlsanity.com/paper/2212.03967/) (2022)
- [Omni-Seg: A Scale-aware Dynamic Network for Renal Pathological Image Segmentation](https://mlsanity.com/paper/2206.13632/) (2022)
- [PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation](https://mlsanity.com/paper/2402.19286/) (2024)
- [Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model](https://mlsanity.com/paper/2505.04522/) (2025)
- [HATs: Hierarchical Adaptive Taxonomy Segmentation for Panoramic Pathology Image Analysis](https://mlsanity.com/paper/2407.00596/) (2024)
- [AGAR a microbial colony dataset for deep learning detection](https://mlsanity.com/paper/2108.01234/) (2021)
- [Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis](https://mlsanity.com/paper/2106.07049/) (2021)
- [Glo-In-One-v2: Holistic Identification of Glomerular Cells, Tissues, and Lesions in Human and Mouse Histopathology](https://mlsanity.com/paper/2411.16961/) (2024)
- [Sample selection for efficient image annotation](https://mlsanity.com/paper/2105.04678/) (2021)
- [PFPs: Prompt-guided Flexible Pathological Segmentation for Diverse Potential Outcomes Using Large Vision and Language Models](https://mlsanity.com/paper/2407.09979/) (2024)
- [Variability Matters : Evaluating inter-rater variability in histopathology for robust cell detection](https://mlsanity.com/paper/2210.05175/) (2022)
- [Rapid model transfer for medical image segmentation via iterative human-in-the-loop update: from labelled public to unlabelled clinical datasets for multi-organ segmentation in CT](https://mlsanity.com/paper/2204.06243/) (2022)
- [Active Learning Based Domain Adaptation for Tissue Segmentation of Histopathological Images](https://mlsanity.com/paper/2303.05225/) (2023)
- [Large-scale cervical precancerous screening via AI-assisted cytology whole slide image analysis](https://mlsanity.com/paper/2407.19512/) (2024)
- [IRS: Incremental Relationship-guided Segmentation for Digital Pathology](https://mlsanity.com/paper/2505.22855/) (2025)
- [Whole-Slide Image Analysis under Realistic Few-Shot Annotation Protocols](https://mlsanity.com/paper/2608.30420/) (2026)
- [Histo-fetch -- On-the-fly processing of gigapixel whole slide images simplifies and speeds neural network training](https://mlsanity.com/paper/2102.11433/) (2021)
- [Code-free development and deployment of deep segmentation models for digital pathology](https://mlsanity.com/paper/2111.08430/) (2021)
- [PatchSorter: A High Throughput Deep Learning Digital Pathology Tool for Object Labeling](https://mlsanity.com/paper/2307.07528/) (2023)
- [GLAM: Glomeruli Segmentation for Human Pathological Lesions using Adapted Mouse Model](https://mlsanity.com/paper/2407.18390/) (2024)
- [Deep Learning-Based Quantitative Assessment of Renal Chronicity Indices in Lupus Nephritis](https://mlsanity.com/paper/2503.21818/) (2025)

## Abstract (arXiv)

Neural networks promise to bring robust, quantitative analysis to medical fields, but adoption is limited by the technicalities of training these networks. To address this translation gap between medical researchers and neural networks in the field of pathology, we have created an intuitive interface which utilizes the commonly used whole slide image (WSI) viewer, Aperio ImageScope (Leica Biosystems Imaging, Inc.), for the annotation and display of neural network predictions on WSIs. Leveraging this, we propose the use of a human-in-the-loop strategy to reduce the burden of WSI annotation. We track network performance improvements as a function of iteration and quantify the use of this pipeline for the segmentation of renal histologic findings on WSIs. More specifically, we present network performance when applied to segmentation of renal micro compartments, and demonstrate multi-class segmentation in human and mouse renal tissue slides. Finally, to show the adaptability of this technique to other medical imaging fields, we demonstrate its ability to iteratively segment human prostate glands from radiology imaging data.