WCODE-PIA, that focuses on the learning of incomplete annotations, is a medical image segmentation framework improved from WCODE.
- This project focuses on the incomplete labeling task, in which the foreground area is partially labeled and the remaining pixels are considered as the background.
| Title | Implementation | Web |
|---|---|---|
| Weakly Supervised Lymph Nodes Segmentation Based on Partial Instance Annotations with Pre-trained Dual-branch Network and Pseudo Label Learning | DBDMP | MELBA2024 |
| ReCo-I2P: An Incomplete Supervised Lymph Node Segmentation Framework Based on Orthogonal Partial-Instance Annotation | ReCo-I2P | MICCAI2025 (Oral) |
Some implementations of compared state-of-the-art (SOTA) methods can be found here.
IA - Inaccurate label, IC - Incomplete label
| Category | Authors | Title | Implementation | Web |
|---|---|---|---|---|
| IA | B. Han et al. | Co-teaching: robust training of deep neural networks with extremely noisy labels | Coteaching | NeurIPS2018 |
| IA | C. Fang et al. | Reliable Mutual Distillation for Medical Image Segmentation Under Imperfect Annotations | RMD | TMI2023 |
| IA | T. Weng et al. | Accurate Segmentation of Optic Disc and Cup from Multiple Pseudo-labels by Noise-aware Learning | MPNN | CSCWD2024 |
| IC | C. Liu et al. | AIO2: Online Correction of Object Labels for Deep Learning With Incomplete Annotation in Remote Sensing Image Segmentation | None | TGRS2024 |
| IC | H. Zhou et al. | Unsupervised domain adaptation for histopathology image segmentation with incomplete labels | None | CBM2024 |
--- Email: [email protected]