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Updated: Aug 16, 2025

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

Breast Cancer Dataset, Classification and Detection Using Deep Learning.

Muhammad Shahid Iqbal¹, Waqas Ahmad², Roohallah Alizadehsani³

¹Department of Computer Science and Information Technology, Women University AJK, Bagh 12500, Pakistan.

Healthcare (Basel, Switzerland)

|December 23, 2022

Summary

This summary is machine-generated.

This review explores deep learning in computational pathology for breast cancer diagnosis, highlighting methods, datasets, and code to advance clinical practice.

Keywords:

breast cancer diagnosis deep learning machine learning malignant growth tumor

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Area of Science:

Computational pathology
Digital pathology
Clinical informatics

Background:

Pathology and laboratory medicine are crucial for cancer diagnosis.
Clinical informatics integrates research into practice for improved patient treatment.
Computational pathology merges whole slide imaging, multi-omics, and health informatics.

Purpose of the Study:

To review computational and digital pathology methods for breast cancer diagnosis.
To focus on deep learning applications in breast cancer diagnosis.
To identify challenges and future directions in deep learning-based diagnosis.

Main Methods:

Review of public datasets for breast cancer diagnosis.
Examination of existing deep learning methodologies for breast cancer diagnosis.
Introduction of publicly available code repositories.

Main Results:

Summary of current deep learning approaches in breast cancer diagnosis.
Identification of relevant datasets and code for computational pathology research.
Overview of the state-of-the-art in deep learning for breast cancer.

Conclusions:

Deep learning shows significant potential in computational pathology for breast cancer diagnosis.
Further research is needed to address current challenges and advance the field.
Integration of computational methods can enhance diagnostic accuracy and patient care.