ArticleJournal of physics. Conference series2024
All-in-SAM: from Weak Annotation to Pixel-wise Nuclei Segmentation with Prompt-based Finetuning.
Article in Journal of physics. Conference series, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Self-Refining Segment Anything Model for Nuclei Segmentation as Contrastive Learning Approach to Label-Efficient Pathological Imaging.Diagnostics (Basel, Switzerland) · 2026Article
- Weak-to-strong generalization enables fully automated training of multi-head mask-RCNN model for segmenting densely overlapping cell nuclei in multiplex whole-slice brain images.Frontiers in bioinformatics · 2026Article
- Research on Medical Image Segmentation Based on SAM and Its Future Prospects.Bioengineering (Basel, Switzerland) · 2025Review
- Lightweight Evolving U-Net for Next-Generation Biomedical Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- From CNNs to SAM: A Survey of Deep Learning Techniques for Liver Tumor Segmentation in CT Images.IEEE access : practical innovations, open solutions · 2025Article
- Data-driven nucleus subclassification on colon hematoxylin and eosin using style-transferred digital pathology.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- HATs: Hierarchical Adaptive Taxonomy Segmentation for Panoramic Pathology Image Analysis.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Foundational Models for Pathology and Endoscopy Images: Application for Gastric Inflammation.Diagnostics (Basel, Switzerland) · 2024Review
- Enhancing Medical Imaging Segmentation with GB-SAM: A Novel Approach to Tissue Segmentation Using Granular Box Prompts.Cancers · 2024Article
- Leverage Weakly Annotation to Pixel-wise Annotation via Zero-shot Segment Anything Model for Molecular-empowered Learning.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- FNPC-SAM: Uncertainty-Guided False Negative/Positive Control for SAM on Noisy Medical Images.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
The Segment Anything Model (SAM) is a recently proposed prompt-based segmentation model in a generic zero-shot segmentation approach. With the zero-shot segmentation capacity, SAM achieved impressive flexibility and precision on various segmentation tasks. However, the current pipeline requires manual prompts during the inference stage, which is still resource intensive for biomedical image segmentation. In this paper, instead of using prompts during the inference stage, we introduce a pipeline that utilizes the SAM, called all-in-SAM, through the entire AI development workflow (from annotation generation to model finetuning) without requiring manual prompts during the inference stage. Specifically, SAM is first employed to generate pixel-level annotations from weak prompts (e.g., points, bounding box). Then, the pixel-level annotations are used to finetune the SAM segmentation model rather than training from scratch. Our experimental results reveal two key findings: 1) the proposed pipeline surpasses the state-of-the-art methods in a nuclei segmentation task on the public Monuseg dataset, and 2) the utilization of weak and few annotations for SAM finetuning achieves competitive performance compared to using strong pixelwise annotated data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.