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AI Model Development for Digital Pathology

AI Model Development for Digital Pathology

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Course Overview

11/28/2025

Registration closes 11/28/2025

Program Syllabus

Module 1

About This Course

Digital pathology has revolutionized diagnostic workflows by converting histological slides into high-resolution digital images. The integration of AI allows pathologists to analyze large datasets with increased speed, precision, and reproducibility. This workshop covers the fundamentals of AI in pathology, including image preprocessing, feature extraction, and model training for disease detection and classification.
Participants will learn to apply AI algorithms to real-world pathology datasets, develop predictive models, and validate their performance. Emphasis will be placed on practical applications in oncology, hematology, and other clinical specialties, providing insights into improving diagnostic accuracy and enabling personalized patient care.

Module 2

Aim

This workshop aims to provide participants with hands-on experience in developing AI models for digital pathology. It focuses on leveraging machine learning and deep learning techniques to analyze histopathological images, identify disease patterns, and assist in accurate diagnostics, ultimately enhancing clinical decision-making

Module 3

Workshop Objectives

  • Understand digital pathology workflows and challenges.
  • Learn image preprocessing, annotation, and feature extraction techniques.
  • Develop machine learning and deep learning models for histopathological analysis.
  • Evaluate and validate AI models for clinical relevance and accuracy.
  • Apply AI models to real-world pathology datasets and case studies.
Module 4

Workshop Structure

Day 1 – Convolutional Neural Networks

  • CNNs and their role in pathology
  • CNN architecture (convolution, pooling, activation)
  • Pathology-specific challenges: stain variation, magnification levels
  • Data preparation: WSI patching, color normalization, augmentation

Day 2 – Model Building

  • Choosing architectures (ResNet, VGG, DenseNet, EfficientNet)
  • Dataset splitting & validation methods
  • Handling class imbalance and selecting evaluation metrics
  • Hands-on: Training a CNN model for tissue classification

Day 3 – Optimization & Transfer Learning

  • Hyperparameter tuning & regularization methods
  • Early stopping and learning rate scheduling
  • Transfer learning: pre-trained models & fine-tuning for pathology
  • Hands-on: Implementing transfer learning + model interpretability (Grad-CAM)
Module 5

Who Should Enrol?

  • Undergraduate/postgraduate degree in Pathology, Biotechnology, Bioinformatics, Medical Imaging, Computational Biology, or related fields.
  • Professionals in healthcare, diagnostics, hospitals, or pharmaceutical sectors.
  • Data scientists and AI/ML engineers interested in medical imaging and pathology applications.
  • Individuals with an interest in AI-assisted clinical diagnostics and healthcare innovation.
Module 6

Important Dates

Module 7

Registration Ends

11/28/2025
IST 7:00 PM

Module 8

Workshop Dates

11/28/2025 – 11/30/2025
IST 8:00 PM

Module 9

Workshop Outcomes

  • Ability to develop AI models for pathology image analysis.
  • Knowledge of preprocessing and annotating digital pathology slides.
  • Skills in training, testing, and validating AI models for clinical application.
  • Understanding the integration of AI models in pathology workflows.
  • Insights into AI-driven precision diagnostics and personalized patient care.
Module 10

Meet Your Mentor(s)

Prof. Kumud Malhotra

Professor & Dean

Prof. Kumud Malhotra, Dean of the University Institute of Physical and Life Sciences with 30 years of experience is an academician and administrator and has attained the highest echelons in the educational sector by managing senior positions, like Director, Dean, Managing Editor, or Editor-in-Chief . . . for content development and journal publication. She completed her Ph. D on “Genetic Epidemiology of Malaria in Some Population Groups of Delhi and Tribal Groups of Tarai Region of Nainital District” and Post Doctorate from Delhi University as an ICMR and UGC fellow.
Her expertise domains are Biotechnology, Genetics, Bioinformatics, Forensic  Science, Molecular Biology, and its allied areas. She has obtained Certifications in the areas of Genetic Engineering, Molecular Biology, Microarrays, Proteomics, and Parasitology from IGIB, NICD, MRC, and Delhi University. Down the lane, her achievements and accolades are the best employee and faculty while working at Bioinformatics Institute of India and RNB GLOBAL University as a Dean and other prestigious awards. Her contribution to the establishment of the School of Basic and Applied Sciences and Agricultural Sciences along with the approval from ICAR at the new University RNBGLOBAL
University was acknowledged by the Management.
Apart from teaching and academics her experience in content development and e-learning for various renowned universities and organizations like Banasthali Vidyapith, GOLS Academy, Biionline, etc. is an added credential of hers. Dr. Kumud Malhotra has been a Resource Person in Course Development Committees (for graduate and post-graduate programs) for Indira Gandhi National Open University (IGNOU), Jamia Hamdard along with other credits like Member of various committees like Academic Council, BOS, Research Committee, etc. for the IGNOU, IAMR. She has demonstrated caliber as a resourceful Technical Collaborator for the conduction of the Workshop on Bioinformatics at
Bundelkhand University and in Engineering College in Durg, Chhattisgarh with an appreciation letter from the university.
She is a member of the editorial board of various journals and has presented Papers at International Conferences on Genetics, Parasitology, Clinical Trials, Pharmacovigilance, Biotechnology, etc. Dr. Malhotra has delivered keynote addresses at CII, International Conferences, and member of various professional bodies Genetics Society, Indian Science Congress, International Society for Malariology and Parasitology, IAA, SBTI, etc. She has guided many students for Ph.D. and internships and got the projects from ICMR.

Module 11

Fee Structure

Module 12

Student Fee

₹1499 | $55

Module 13

Ph.D. Scholar / Researcher Fee

₹2499 | $65

Module 14

Academician / Faculty Fee

₹3499 | $80

Module 15

Industry Professional Fee

₹4499 | $90

Module 16

What You’ll Gain

  • Live & recorded sessions
  • e-Certificate upon completion
  • Post-workshop query support
  • Hands-on learning experience
Module 17

Join Our Hall of Fame!

Take your research to the next level with NanoSchool.

Module 18

Publication Opportunity

Get published in a prestigious open-access journal.

Module 19

Centre of Excellence

Become part of an elite research community.

Module 20

Networking & Learning

Connect with global researchers and mentors.

Module 21

Global Recognition

Worth ₹20,000 / $1,000 in academic value.

Module 22

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Instructor

Lead Instructor

Dr. Sarah Chen

PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.

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AI Model Development for Digital Pathology

Professional Certification Program

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FormatLive + Recorded
📅
Duration8 Weeks
📜
CertificationVerified
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