# Adaptive Pseudo Label Selection for Individual Unlabeled Data by Positive and Unlabeled Learning

arXiv: https://arxiv.org/abs/2508.07548 · HTML: https://mlsanity.com/paper/2508.07548/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 11 Aug 2025.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Positive-Unlabeled Learning with Non-Negative Risk Estimator](https://mlsanity.com/paper/1703.00593/) (2017)
- [An Overview of Deep Semi-Supervised Learning](https://mlsanity.com/paper/2006.05278/) (2020)
- [A deep learning system for differential diagnosis of skin diseases](https://mlsanity.com/paper/1909.05382/) (2019)
- [Min-Max Similarity: A Contrastive Semi-Supervised Deep Learning Network for Surgical Tools Segmentation](https://mlsanity.com/paper/2203.15177/) (2022)
- [Weakly Supervised Cell Instance Segmentation by Propagating from Detection Response](https://mlsanity.com/paper/1911.13077/) (2019)
- [Positive-unlabeled Learning for Cell Detection in Histopathology Images with Incomplete Annotations](https://mlsanity.com/paper/2106.15918/) (2021)
- [Cell Detection from Imperfect Annotation by Pseudo Label Selection Using P-classification](https://mlsanity.com/paper/2107.09289/) (2021)

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

- [VesselFusion: Diffusion Models for Vessel Centerline Extraction from 3D CT Images](https://mlsanity.com/paper/2603.08135/) (2026)

## Abstract (arXiv)

This paper proposes a novel pseudo-labeling method for medical image segmentation that can perform learning on ``individual images'' to select effective pseudo-labels. We introduce Positive and Unlabeled Learning (PU learning), which uses only positive and unlabeled data for binary classification problems, to obtain the appropriate metric for discriminating foreground and background regions on each unlabeled image. Our PU learning makes us easy to select pseudo-labels for various background regions. The experimental results show the effectiveness of our method.