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Updated: Mar 25, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

MOSAIC: Multi-scale orientation-aware segmentation and instance classification network for histopathological image

Arbab Sufyan Wadood¹, Mohammad Faizal Ahmad Fauzi², Lai Kuan Wong³

¹Faculty of Artificial Intelligence and Engineering, Multimedia University, 63100, Cyberjaya, Selangor, Malaysia; Department of Computer Science, Balochistan University of Information Technology, Engineering and Management Sciences (BUITEMS), Quetta, Pakistan.

Computer Methods and Programs in Biomedicine

|March 23, 2026

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This summary is machine-generated.

This study introduces a novel computational framework for robust nuclei segmentation and classification in histopathology images, improving accuracy across varying scales and orientations without retraining. The MOSAIC model enhances boundary precision and instance separation for better diagnostic insights.

Area of Science:

Biomedical image analysis
Computational pathology
Deep learning for medical imaging

Background:

Accurate nuclei segmentation and classification are crucial for histopathology image analysis.
Existing models struggle with scale variability, diverse morphologies, and arbitrary orientations.
There's a need for robust computational frameworks that overcome these limitations without complex supervision.

Purpose of the Study:

To develop a unified computational framework for nuclei segmentation and instance classification.
The framework aims for robustness against magnification variability, arbitrary orientations, and long-range dependencies.
To achieve this without multi-magnification supervision or retraining.

Main Methods:

Proposed a multi-scale orientation-aware segmentation and instance classification (MOSAIC) framework.

Keywords:

Breast cancer Computational pathology Digital pathology Instance classification Nuclei segmentation

Related Experiment Videos

Last Updated: Mar 25, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

Integrated hierarchical context extraction, rotation-aware feature fusion, and transformer-based modeling.

Utilized single-native magnification training for multi-scale contextual cues.

Main Results:

Achieved a mean Dice coefficient of 0.862, Aggregated Jaccard Index of 0.721, and Panoptic Quality of 0.647.
Demonstrated 3%-7% improvement over baseline methods across multiple datasets.
Showcased efficient performance with 0.175s inference time and 3.7 GB peak memory.

Conclusions:

Orientation-aware multi-scale fusion and long-range contextual modeling enhance boundary precision and instance separation.
The MOSAIC framework improves classification consistency for heterogeneous nuclear morphologies.
The proposed design generalizes reliably across challenging and diverse tissue appearances.