# Tumor segmentation on whole slide images: training or prompting?

arXiv: https://arxiv.org/abs/2402.13932 · HTML: https://mlsanity.com/paper/2402.13932/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 21 references that are in the map.
- First posted on arXiv 21 Feb 2024.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020)
- [Exploring Visual Prompts for Adapting Large-Scale Models](https://mlsanity.com/paper/2203.17274/) (2022)
- [Visual Prompting via Image Inpainting](https://mlsanity.com/paper/2209.00647/) (2022)
- [SegGPT: Segmenting Everything In Context](https://mlsanity.com/paper/2304.03284/) (2023)
- [What Makes Good Examples for Visual In-Context Learning?](https://mlsanity.com/paper/2301.13670/) (2023)
- [Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs](https://mlsanity.com/paper/2103.03129/) (2021)
- [Renal Cell Carcinoma Detection and Subtyping with Minimal Point-Based Annotation in Whole-Slide Images](https://mlsanity.com/paper/2008.05332/) (2020)

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

- [FlexICL: A Flexible Visual In-context Learning Framework for Elbow and Wrist Ultrasound Segmentation](https://mlsanity.com/paper/2510.26049/) (2025)

## Abstract (arXiv)

Tumor segmentation stands as a pivotal task in cancer diagnosis. Given the immense dimensions of whole slide images (WSI) in histology, deep learning approaches for WSI classification mainly operate at patch-wise or superpixel-wise level. However, these solutions often struggle to capture global WSI information and cannot directly generate the binary mask. Downsampling the WSI and performing semantic segmentation is another possible approach. While this method offers computational efficiency, it necessitates a large amount of annotated data since resolution reduction may lead to information loss. Visual prompting is a novel paradigm that allows the model to perform new tasks by making subtle modifications to the input space, rather than adapting the model itself. Such approach has demonstrated promising results on many computer vision tasks. In this paper, we show the efficacy of visual prompting in the context of tumor segmentation for three distinct organs. In comparison to classical methods trained for this specific task, our findings reveal that, with appropriate prompt examples, visual prompting can achieve comparable or better performance without extensive fine-tuning.