# DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Images

arXiv: https://arxiv.org/abs/2410.03058 · HTML: https://mlsanity.com/paper/2410.03058/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 68 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Builds on (most cited first, 27 of 27)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [Medical Transformer: Gated Axial-Attention for Medical Image Segmentation](https://mlsanity.com/paper/2102.10662/) (2021)
- [Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces](https://mlsanity.com/paper/1903.03545/) (2019)
- [An Unsupervised Learning Model for Deformable Medical Image Registration](https://mlsanity.com/paper/1802.02604/) (2018)
- [Data augmentation using learned transformations for one-shot medical image segmentation](https://mlsanity.com/paper/1902.09383/) (2019)
- [SAM-Med2D](https://mlsanity.com/paper/2308.16184/) (2023)
- [Logit Standardization in Knowledge Distillation](https://mlsanity.com/paper/2403.01427/) (2024)
- [Micro-Net: A unified model for segmentation of various objects in microscopy images](https://mlsanity.com/paper/1804.08145/) (2018)
- [A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches](https://mlsanity.com/paper/2102.10553/) (2021)
- [Correlation-aware Coarse-to-fine MLPs for Deformable Medical Image Registration](https://mlsanity.com/paper/2406.00123/) (2024)
- [LT-Net: Label Transfer by Learning Reversible Voxel-wise Correspondence for One-shot Medical Image Segmentation](https://mlsanity.com/paper/2003.07072/) (2020)
- [Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning](https://mlsanity.com/paper/2305.18962/) (2023)
- [Weakly Supervised Cell Instance Segmentation by Propagating from Detection Response](https://mlsanity.com/paper/1911.13077/) (2019)
- [Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy](https://mlsanity.com/paper/2312.04823/) (2023)
- [CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation](https://mlsanity.com/paper/2209.11359/) (2022)
- [Provably Convergent Learned Inexact Descent Algorithm for Low-Dose CT Reconstruction](https://mlsanity.com/paper/2104.12939/) (2021)
- [Recursive Refinement Network for Deformable Lung Registration between Exhale and Inhale CT Scans](https://mlsanity.com/paper/2106.07608/) (2021)
- [Exploring Unsupervised Cell Recognition with Prior Self-activation Maps](https://mlsanity.com/paper/2308.11144/) (2023)
- [Segmentation with Residual Attention U-Net and an Edge-Enhancement Approach Preserves Cell Shape Features](https://mlsanity.com/paper/2001.05548/) (2020)
- [Semi-supervised Learning with the EM Algorithm: A Comparative Study between Unstructured and Structured Prediction](https://mlsanity.com/paper/2008.12442/) (2020)
- [Structural constrained virtual histology staining for human coronary imaging using deep learning](https://mlsanity.com/paper/2211.06737/) (2022)
- [Point-supervised Single-cell Segmentation via Collaborative Knowledge Sharing](https://mlsanity.com/paper/2304.10671/) (2023)

## Built on by (most cited first, 5 of 5)

- [Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds](https://mlsanity.com/paper/2410.12779/) (2024)
- [WISE: A Framework for Gigapixel Whole-Slide-Image Lossless Compression](https://mlsanity.com/paper/2503.18074/) (2025)
- [Dispersion Loss Counteracts Embedding Condensation and Improves Generalization in Small Language Models](https://mlsanity.com/paper/2602.00217/) (2026)
- [Frequency Prior Guided Matching: A Data Augmentation Approach for Generalizable Semi-Supervised Polyp Segmentation](https://mlsanity.com/paper/2508.06517/) (2025)
- [RNAGenScape: Property-Guided, Optimized Generation of mRNA Sequences with Manifold Langevin Dynamics](https://mlsanity.com/paper/2510.24736/) (2025)

## Abstract (arXiv)

The proliferation of digital microscopy images, driven by advances in automated whole slide scanning, presents significant opportunities for biomedical research and clinical diagnostics. However, accurately annotating densely packed information in these images remains a major challenge. To address this, we introduce DiffKillR, a novel framework that reframes cell annotation as the combination of archetype matching and image registration tasks. DiffKillR employs two complementary neural networks: one that learns a diffeomorphism-invariant feature space for robust cell matching and another that computes the precise warping field between cells for annotation mapping. Using a small set of annotated archetypes, DiffKillR efficiently propagates annotations across large microscopy images, reducing the need for extensive manual labeling. More importantly, it is suitable for any type of pixel-level annotation. We will discuss the theoretical properties of DiffKillR and validate it on three microscopy tasks, demonstrating its advantages over existing supervised, semi-supervised, and unsupervised methods. The code is available at https://github.com/KrishnaswamyLab/DiffKillR.