Distribution-based deep multiple instance learning for tumor proportion scoring in NSCLC

📰 ArXiv cs.AI

Learn how to apply distribution-based deep multiple instance learning for tumor proportion scoring in NSCLC using MIL and deep learning techniques

advanced Published 29 Jun 2026

Action Steps

Apply multiple instance learning (MIL) to predict tumor proportion scores at the slide level
Use deep learning architectures to extract features from histopathology images
Implement distribution-based methods to model the uncertainty of TPS scores
Train and evaluate the model using a dataset of annotated histopathology images
Compare the performance of the proposed method with existing MIL-based approaches

Who Needs to Know This

This micro-lesson is suitable for data scientists, machine learning engineers, and researchers working on medical image analysis, particularly those interested in applying deep learning techniques to non-small cell lung cancer diagnosis and treatment planning.

Key Insight

💡 Distribution-based deep multiple instance learning can effectively predict tumor proportion scores in NSCLC by modeling the uncertainty of TPS scores

Full Article

Title: Distribution-based deep multiple instance learning for tumor proportion scoring in NSCLC

Abstract:
arXiv:2606.27579v1 Announce Type: cross Abstract: Accurate assessment of tumor proportion score (TPS) in non-small cell lung cancer (NSCLC) is critical for treatment planning and prognosis. Key challenges include the tedious manual work required to annotate each slide, combined with the limited number of experts certified for this task. Multiple instance learning (MIL) has proven to be an effective approach for predicting TPS scores at the slide level; however, existing methods struggle with non

Read full paper → ← Back to Reads