Deep Learning for Histopathology: WSIs, MIL & Transformers
From Gigapixel Slides to Intelligent Predictions
About This Course
Digital histopathology has rapidly evolved with the integration of artificial intelligence, enabling scalable and reproducible analysis of whole-slide images. Traditional pixel-level annotations are costly and time-consuming, making modern approaches like MIL and transformers essential for learning directly from slide-level labels. These methods are now widely used in cancer diagnosis, grading, prognosis, and biomarker discovery.
This workshop provides a structured, dry-lab–only learning experience covering WSI preprocessing, patch extraction, feature learning, MIL pipelines, and transformer architectures tailored for histopathology. Through real datasets and guided hands-on sessions, participants will learn best practices for model evaluation, interpretability, and clinical relevance—bridging the gap between AI research and digital pathology applications.
Aim
This workshop aims to equip participants with practical and theoretical knowledge of applying deep learning techniques to digital histopathology data, with a focus on whole-slide images (WSIs). The program introduces modern approaches such as Multiple Instance Learning (MIL) and Transformer-based models to handle gigapixel pathology images under weak supervision. Participants will gain hands-on experience in building, evaluating, and interpreting AI models for cancer detection and pathology research.
Workshop Objectives
- Understand WSI formats and challenges in histopathology AI.
- Perform patch extraction and deep feature representation from WSIs.
- Implement MIL models for weakly supervised slide classification.
- Apply transformer architectures for slide-level prediction.
- Evaluate and interpret models using clinically relevant metrics.
Workshop Structure
Day 1 – Pathology Foundation Models & Self-Supervised WSI Representation
- Pathology Foundation Models (PFMs) for WSIs (pretrained ViT/CNN for feature extraction).
- Self-supervised & contrastive learning (DINO, MoCo, SimCLR) for robust, stain-invariant features.
- Multi-scale WSI tiling and patch-level representation.
- Building reusable feature stores for downstream tasks.
Hands-On:
- WSI patching + extracting embeddings using a pretrained PFM.
- Visualizing embedding clusters (t-SNE / UMAP).
Day 2 – Transformers & MIL for Slide-Level Tumor Detection
- Attention-based MIL, cross-scale MIL, graph-based MIL for gigapixel WSIs.
- Transformers for WSIs: patch embeddings → token sequences.
- Weak supervision using slide-level labels only.
- Clinical evaluation metrics: AUC, F1, calibration, patient-level CV.
Hands-On :
- Training an attention-based MIL model for tumor classification.
- Building a transformer-based slide classifier.
- Comparing MIL vs transformer performance (ROC, confusion matrix, sensitivity/specificity).
Day 3 – Multimodal, Generative & Explainable AI in Pathology
- Multimodal PFMs combining WSI + clinical/genomic/text embeddings.
- Task-adaptive PFMs: LoRA, adapters, prompt-based fine-tuning.
- Explainable AI: Grad-CAM, attention maps, uncertainty estimation.
Hands-On :
- Building a simple multimodal fusion model (WSI features + clinical variables/text).
- Generating interpretability outputs (Grad-CAM, attention heatmaps).
Who Should Enrol?
- Undergraduate/postgraduate degree in Biotechnology, Bioinformatics, Computational Biology, Biomedical Engineering, Pathology, Life Sciences, or related fields.
- Researchers and professionals working in digital pathology, cancer research, medical imaging, or healthcare AI.
- Data scientists and AI/ML engineers interested in medical image analysis.
- Individuals with an interest in the intersection of deep learning and pathology.
Important Dates
Registration Ends
01/13/2026
IST 7:00 PM
Workshop Dates
01/13/2026 – 01/15/2026
IST 8:00 PM
Workshop Outcomes
- Gain hands-on experience with WSI preprocessing and patch-level learning.
- Build MIL and transformer-based models for slide-level prediction.
- Learn to evaluate models using AUC, sensitivity, and validation strategies.
- Generate interpretable outputs such as attention maps and heatmaps.
- Be prepared to apply AI methods to real-world pathology datasets.
Fee Structure
Student Fee
₹1799 | $70
Ph.D. Scholar / Researcher Fee
₹2799 | $80
Academician / Faculty Fee
₹3799 | $95
Industry Professional Fee
₹4799 | $110
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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