Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Nuclear Localization Signals and Import

Nuclear Localization Signals and Import

Proteins targeted to the nucleus carry short stretches of amino acid sequences called the nuclear localization signal or NLS. Classical nuclear localization signals are of two types: monopartite and bipartite NLS. Monopartite classical NLS (cNLS) consists of a single cluster of 4-8 amino acids. Bipartite cNLS consists of two clusters of 2-3 amino acids and a 9-12 residue long proline-rich linker bridging the two clusters. Signal clusters are rich in positively charged amino acids such as...

Aggregates Classification

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Corrigendum: Referenceless MR thermometry-a comparison of five methods (2017<i>Phys. Med. Biol</i>.<b>62</b>1-16).

Physics in medicine and biology·2026

Same author

Predicting early-stage breast cancer disease-free survival and adjuvant therapy benefit from multimodal information using deep learning.

NPJ breast cancer·2026

Same author

Lumi-Guide: An Artificial-Intelligence-Driven Multimodal Framework for Optimizing Personalized Neoadjuvant Therapy Decision-Making in Luminal Breast Cancer.

Research (Washington, D.C.)·2026

Same author

Response to letter.

The British journal of radiology·2026

Same author

Tailoring the Extent of Lymphadenectomy for Esophageal Squamous Cell Carcinoma: Insights From a Comparative Study of Neoadjuvant Chemo-Immunotherapy and Surgery Cohort.

Thoracic cancer·2026

Same author

BLADE: Breast Lesion Analysis with Domain Expertise for DCE-MRI Diagnosis.

IEEE journal of biomedical and health informatics·2026

Same journal

Embracing intra-class heterogeneity for semi-supervised medical image segmentation: From diversity to precision.

Medical image analysis·2026

Same journal

Real-time patient-specific microwave ablation zone prediction via a unified bioheat solver and MRI-informed perturbation learning.

Medical image analysis·2026

Same journal

Generative morphodynamic forecasting enables robust zero-shot volumetric medical segmentation.

Medical image analysis·2026

Same journal

ContiMorph: An unsupervised learning framework for cardiac motion tracking with time-continuous diffeomorphism.

Medical image analysis·2026

Same journal

MedP-CLIP: Medical CLIP with region-aware prompt integration.

Medical image analysis·2026

Same journal

Multi-organ guided diagnosis of mild cognitive impairment via hierarchical alignment and knowledge distillation.

Medical image analysis·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 26, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

SMILE: Cost-sensitive multi-task learning for nuclear segmentation and classification with imbalanced annotations.

Xipeng Pan¹, Jijun Cheng², Feihu Hou³

¹School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, Guangxi 541004, China; Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, Guangdong 510080, China.

Medical Image Analysis

|June 22, 2023

Summary

This summary is machine-generated.

This study introduces a novel framework (SMILE) to improve nuclear segmentation and classification in whole slide images by addressing data heterogeneity. The method enhances feature representation and segmentation accuracy for biological and clinical applications.

Keywords:

Cost-sensitive Imbalanced annotation Multi-task correlation attention Nuclear segmentation and classification

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Related Experiment Videos

Last Updated: Jul 26, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

Computational pathology
Digital pathology
Biomedical image analysis

Background:

Accurate nuclear segmentation and classification in whole slide images (WSIs) are vital for biological analysis, clinical diagnosis, and precision medicine.
Current methods struggle with nuclear heterogeneity, specifically imbalanced data distribution and diverse morphology, leading to dominated minority classes and fragile segmentation.

Purpose of the Study:

To develop a robust framework to address data heterogeneity in nuclear segmentation and classification of WSIs.
To improve the accuracy and reliability of nuclear analysis in complex biological samples.

Main Methods:

Proposed a cost-Sensitive MultI-task LEarning (SMILE) framework.
Introduced a multi-task correlation attention (MTCA) mechanism for enhanced feature representation by interacting relevant tasks.
Implemented a cost-sensitive learning strategy to penalize minority class misclassification.
Developed a coarse-to-fine marker-controlled watershed post-processing step for improved segmentation of large nuclei with unclear contours.

Main Results:

The SMILE framework achieved state-of-the-art performance on the CoNSeP and MoNuSAC 2020 datasets.
Demonstrated significant improvements in handling imbalanced data and diversified nuclear morphology.
The proposed MTCA and post-processing steps effectively enhanced segmentation and classification accuracy.

Conclusions:

The SMILE framework offers a powerful solution for nuclear heterogeneity challenges in WSI analysis.
This approach advances the capabilities of computational pathology for more accurate diagnostics and precision medicine.
The developed methods provide a foundation for future research in automated histopathological image analysis.