AMN: An Adaptive Multi-Scale Fusion Network with Boundary and Uncertainty Modeling for Nuclei Segmentation
arXiv:2606.07633v1 Announce Type: cross Abstract: Accurate classification of nuclei subtypes in histopathology images is critical for downstream tasks including tumor grading, immune infiltrate quantification, and prognosis prediction. Existing approaches rely on either convolutional or transformer-based encoders in isolation, limiting their ability to simultaneously capture fine-grained local texture and long-range spatial context. We present AMN (Adaptive Multi-Scale Nuclei Network), a dual-encoder segmentation framework that jointly leverages a Swin Transformer and a ResNet-50 feature pyram
The continuous advancements in AI, particularly in computer vision and deep learning architectures, enable more sophisticated medical image analysis techniques.
Improved nuclei segmentation directly enhances the accuracy of diagnostic and prognostic predictions for critical diseases like cancer, impacting healthcare outcomes and drug discovery.
This research introduces a more robust method for nuclei segmentation by combining multi-scale feature extraction and boundary/uncertainty modeling, potentially leading to more reliable AI-assisted pathology.
- · AI-powered diagnostics companies
- · Medical research institutions
- · Oncology patients
- · Pharmaceutical companies
- · Traditional pathology methods
More precise and automated analysis of histopathology images becomes possible.
Accelerated development of new cancer therapies and better personalized treatment plans emerge.
Enhanced AI capabilities could reduce human error in diagnoses and lead to earlier disease detection globally.
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Read at arXiv cs.AI