Label-Efficient Hybrid-Supervised Learning for Medical Image Segmentation
Authors: Junwen Pan, Qi Bi, Yanzhan Yang, Pengfei Zhu, Cheng Bian2026-2034
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two hybrid-supervised medical segmentation datasets demonstrate that with only 10% strong labels, the proposed framework can leverage the weak labels efficiently and achieve competitive performance against the 100% strong-label supervised scenario. |
| Researcher Affiliation | Collaboration | Junwen Pan*1,2, Qi Bi3*, Yanzhan Yang1, Pengfei Zhu2, Cheng Bian1 1Xiaohe Healthcare, Byte Dance 2College of Intelligence and Computing, Tianjin University 3School of Remote Sensing and Information Engineering, Wuhan University |
| Pseudocode | Yes | Algorithm 1: DII Learning Require: strongly-annotated dataset DS, weakly-annotated dataset DW , DIIs = {γk|γk [0, 1], k [1, M]}, network parameters , DII update interval , iteration steps T, and learning rates , . 1: for t 1...T do 2: Xbatch Batch Sample(DS DW ) 3: // Lower-level (DCR) gradient descent step 4: L(Xbatch, , ) 5: if (t mod ) = 0 then 6: continue 7: end if 8: 9: XS Batch Sample(DS) 10: // Estimate mean gradients on DS 11: g S L(XS, ) 12: // Calculate per-instance gradients on DW 13: gk ℓ(xk, yk, ), k {1, ..., M} 14: // Estimate inverse Hessian matrix 15: H 1 I 16: // Estimate upper-level gradients w.r.t. DIIs 17: L(XS, ( )) γk g S H 1 gk, k {1, ..., M} 18: // Upper-level gradient descent step 19: γk γk L(XS, ( )) γk , k {1, ..., M} 20: end for |
| Open Source Code | No | The paper does not provide any explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | The hybrid-supervised polyp segmentation dataset has been built from two public available colonoscopic polyp datasets. The CVCEndo Scene Still (Vazquez et al. 2017) includes 912 images with elaborately annotated pixel-level labels. ... The hybrid-supervised AS-OCT segmentation dataset is modified from the training set of the Angle closure Glaucoma Evaluation (AGE) Challenge (Fu et al. 2019), which contains over 3200 AS-OCT images with annotations of the closure classification and the coordinates of scleral spurs. |
| Dataset Splits | Yes | For the AS-OCT segmentation task: 'Then, we follow the same partition protocol in which 60% of the data is used for training, 20% for validation, and the rest 20% for test.' |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. It only mentions implementing the algorithm based on the PyTorch framework. |
| Software Dependencies | No | The paper mentions 'Py Torch framework (Paszke, Gross, and et al. 2019)' and 'Deep Labv3+ structure (Chen et al. 2018)' but does not specify version numbers for these software components, which is required for reproducibility. |
| Experiment Setup | Yes | γ1, ..., γM is initialized with 0.5 and clipped to the range of [0, 1]. We adopt vanilla Adam optimizer (Kingma and Ba 2015) to tune DIIs with default betas set to 0.9 and 0.999 respectively. Network parameters are updated iteratively via mini-batch SGD with momentum=0.9, batch size=16 and weight decay=0.00005. The upper-level and lower-level learning rates are initially set to 0.1 and 0.002 by default, respectively. We finally chose the optimal configuration with τ = 400 and β = 0.1 in our whole study. ...the Dice score will increase at the beginning and reach the maximum at λ = 4... |