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AI model achieves 2nd place in tumor segmentation and pCR prediction challenge

By PulseAugur Editorial · [1 sources] ·

A team from the Fellow of the Academy of Medical Sciences has detailed their approach for the MAMA-MIA Challenge, focusing on tumor segmentation and pathological complete response (pCR) prediction using dynamic contrast-enhanced MRI. Their segmentation method utilized a nnU-Net ensemble with subtraction-based input, achieving a Dice score of 0.713 and ranking second overall. For pCR prediction, they ensembled 25 pretrained 3D video classifiers, obtaining a combined score of 0.664, though they noted limitations in predicting pCR from baseline DCE-MRI alone. AI

IMPACT Demonstrates advanced AI techniques for medical image analysis, potentially improving diagnostic accuracy in oncology.

RANK_REASON This is a research paper detailing a submission to a challenge, including methodology and results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI model achieves 2nd place in tumor segmentation and pCR prediction challenge

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This is a research paper detailing a submission to a challenge, including methodology and results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Kai Geissler, Raphael Sch\"afer ·

    Subtraction-Based Tumor Segmentation and Lesion-Centered pCR Prediction for the MAMA-MIA Challenge

    arXiv:2608.29162v1 Announce Type: cross Abstract: We describe the submission of team FME to the MAMA-MIA Challenge, which evaluated primary tumor segmentation and prediction of pathological complete response (pCR) from pretreatment dynamic contrast-enhanced breast MRI on an exter…

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