# DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentation

arXiv: https://arxiv.org/abs/2101.09057 · HTML: https://mlsanity.com/paper/2101.09057/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 32 references that are in the map.
- First posted on arXiv 22 Jan 2021.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1710.05006/) (2017)
- [Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation](https://mlsanity.com/paper/1908.10454/) (2019)
- [Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis](https://mlsanity.com/paper/1804.06353/) (2018)
- [Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation](https://mlsanity.com/paper/1706.04737/) (2017)
- [3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training](https://mlsanity.com/paper/1811.12506/) (2018)
- [Cost-Effective Active Learning for Melanoma Segmentation](https://mlsanity.com/paper/1711.09168/) (2017)
- [Cost-Sensitive Active Learning for Intracranial Hemorrhage Detection](https://mlsanity.com/paper/1809.02882/) (2018)
- [A Two-Stream Mutual Attention Network for Semi-supervised Biomedical Segmentation with Noisy Labels](https://mlsanity.com/paper/1807.11719/) (2018)
- [Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy](https://mlsanity.com/paper/1807.06962/) (2018)
- [Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation](https://mlsanity.com/paper/1903.04778/) (2019)
- [Deeply Supervised Active Learning for Finger Bones Segmentation](https://mlsanity.com/paper/2005.03225/) (2020)

## Built on by (most cited first, 27 of 27)

- [Multi-Layer Pseudo-Supervision for Histopathology Tissue Semantic Segmentation using Patch-level Classification Labels](https://mlsanity.com/paper/2110.08048/) (2021)
- [A comprehensive survey on deep active learning in medical image analysis](https://mlsanity.com/paper/2310.14230/) (2023)
- [LE-UDA: Label-efficient unsupervised domain adaptation for medical image segmentation](https://mlsanity.com/paper/2212.02078/) (2022)
- [MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels](https://mlsanity.com/paper/2203.12454/) (2022)
- [Hierarchical Consistency Regularized Mean Teacher for Semi-supervised 3D Left Atrium Segmentation](https://mlsanity.com/paper/2105.10369/) (2021)
- [Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions](https://mlsanity.com/paper/2303.12484/) (2023)
- [Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image Segmentation](https://mlsanity.com/paper/2401.16298/) (2024)
- [Self-supervised Assisted Active Learning for Skin Lesion Segmentation](https://mlsanity.com/paper/2205.07021/) (2022)
- [MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image Segmentation](https://mlsanity.com/paper/2207.01883/) (2022)
- [Semi-Supervised Disease Classification based on Limited Medical Image Data](https://mlsanity.com/paper/2405.04295/) (2024)
- [A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation](https://mlsanity.com/paper/2411.16370/) (2024)
- [Language-guided Medical Image Segmentation with Target-informed Multi-level Contrastive Alignments](https://mlsanity.com/paper/2412.13533/) (2024)
- [Adaptive Mean-Residue Loss for Robust Facial Age Estimation](https://mlsanity.com/paper/2203.17156/) (2022)
- [ACT-Net: Asymmetric Co-Teacher Network for Semi-supervised Memory-efficient Medical Image Segmentation](https://mlsanity.com/paper/2207.01900/) (2022)
- [Correlation-aware active learning for surgery video segmentation](https://mlsanity.com/paper/2311.08811/) (2023)
- [Predictive Accuracy-Based Active Learning for Medical Image Segmentation](https://mlsanity.com/paper/2405.00452/) (2024)
- [ProtoAL: Interpretable Deep Active Learning with prototypes for medical imaging](https://mlsanity.com/paper/2404.04736/) (2024)
- [OCTAve: 2D en face Optical Coherence Tomography Angiography Vessel Segmentation in Weakly-Supervised Learning with Locality Augmentation](https://mlsanity.com/paper/2207.12238/) (2022)
- [Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image Segmentation](https://mlsanity.com/paper/2305.06978/) (2023)
- [Active Learning with Context Sampling and One-vs-Rest Entropy for Semantic Segmentation](https://mlsanity.com/paper/2412.06470/) (2024)
- [Residual Channel Attention Network for Brain Glioma Segmentation](https://mlsanity.com/paper/2205.10758/) (2022)
- [Two Heads are Better than One: A Bio-inspired Method for Improving Classification on EEG-ET Data](https://mlsanity.com/paper/2304.06471/) (2023)
- [Active learning with binary models for real time data labelling](https://mlsanity.com/paper/2203.00439/) (2022)
- [DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep Learning Framework](https://mlsanity.com/paper/2308.14657/) (2023)
- [Foundation Model Makes Clustering A Better Initialization For Cold-Start Active Learning](https://mlsanity.com/paper/2402.02561/) (2024)
- [Adapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuning](https://mlsanity.com/paper/2509.10784/) (2025)
- [Unifying Active Learning and Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2607.25014/) (2026)

## Abstract (arXiv)

Image segmentation is one of the most essential biomedical image processing problems for different imaging modalities, including microscopy and X-ray in the Internet-of-Medical-Things (IoMT) domain. However, annotating biomedical images is knowledge-driven, time-consuming, and labor-intensive, making it difficult to obtain abundant labels with limited costs. Active learning strategies come into ease the burden of human annotation, which queries only a subset of training data for annotation. Despite receiving attention, most of active learning methods generally still require huge computational costs and utilize unlabeled data inefficiently. They also tend to ignore the intermediate knowledge within networks. In this work, we propose a deep active semi-supervised learning framework, DSAL, combining active learning and semi-supervised learning strategies. In DSAL, a new criterion based on deep supervision mechanism is proposed to select informative samples with high uncertainties and low uncertainties for strong labelers and weak labelers respectively. The internal criterion leverages the disagreement of intermediate features within the deep learning network for active sample selection, which subsequently reduces the computational costs. We use the proposed criteria to select samples for strong and weak labelers to produce oracle labels and pseudo labels simultaneously at each active learning iteration in an ensemble learning manner, which can be examined with IoMT Platform. Extensive experiments on multiple medical image datasets demonstrate the superiority of the proposed method over state-of-the-art active learning methods.