Research map / Label-efficient and robust learning / Domain adaptation and generalization
Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-Weighting
Check your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add CyC-PDAM instead of Hou et al. | UDA nuclei instance segmentation · BBBC039V1 → Kumar | AJI: 0.4980 ± 0.1236 -> 0.5610 ± 0.0718 | Helped |
| Add CyC-PDAM instead of Hou et al. | UDA nuclei instance segmentation · BBBC039V1 → Kumar | Pixel-F1: 0.7500 ± 0.0849 -> 0.7882 ± 0.0533 | Helped |
| Add CyC-PDAM instead of Hou et al. | UDA nuclei instance segmentation · BBBC039V1 → Kumar | Object-F1: 0.6890 ± 0.0990 -> 0.7483 ± 0.0525 | Helped |
| Add CyC-PDAM instead of Hou et al. | UDA nuclei instance segmentation · BBBC039V1 → TNBC | AJI: 0.4775 ± 0.1219 -> 0.5672 ± 0.0646 | Helped |
| Add CyC-PDAM instead of CyCADA | UDA nuclei instance segmentation · BBBC039V1 → TNBC | Pixel-F1: 0.7048 ± 0.0946 -> 0.7593 ± 0.0566 | Helped |
| Add CyC-PDAM instead of DDMRL | UDA nuclei instance segmentation · BBBC039V1 → TNBC | Object-F1: 0.6872 ± 0.0347 -> 0.7478 ± 0.0417 | Helped |
| Add nuclei inpainting (ablation) | CyC-PDAM · BBBC039V1 → Kumar | AJI: 0.5042 ± 0.1034 -> 0.5610 ± 0.0718 | Helped |
| Add task re-weighting (ablation) | CyC-PDAM · BBBC039V1 → Kumar | AJI: 0.4969 ± 0.0972 -> 0.5610 ± 0.0718 | Helped |
| Add semantic branch (ablation) | CyC-PDAM · BBBC039V1 → Kumar | AJI: 0.5046± 0.1065 -> 0.5610 ± 0.0718 | Helped |
Closest papers by meaning
- Alleviate and Mining: Rethinking Unsupervised Domain Adaptation for Mitochondria Segmentation from Pseudo-Label Perspective (AAAI 2025 · similarity 0.86)
- Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology (NeurIPS 2024 · similarity 0.86)
- Why Invariance is Not Enough for Biomedical Domain Generalization and How to Fix It (NeurIPS 2026 · similarity 0.86)
- UniCell: Universal Cell Nucleus Classification via Prompt Learning (AAAI 2024 · similarity 0.85)
- Universal Domain Adaptation for Semantic Segmentation (CVPR 2025 · similarity 0.85)
- Incremental Nuclei Segmentation from Histopathological Images via Future-class Awareness and Compatibility-inspired Distillation (CVPR 2024 · similarity 0.85)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.