Camyla autonomously generates research proposals, experiments, and manuscripts in medical image segmentation, outperforming baselines on 24 of 31 recent datasets while producing 40 human-reviewed papers.
MSLesSeg: baseline and benchmarking of a new Multiple Sclerosis Lesion Segmentation dataset.Scientific Data, 12(1):920, May 2025
4 Pith papers cite this work, alongside 15 external citations. Polarity classification is still indexing.
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SIAM achieves state-of-the-art whole-head MRI segmentation of 16 structures including extra-cerebral tissues by training on synthetic data from just six manual templates, matching or exceeding prior methods on 301 scans across eight heterogeneous datasets.
CATMIL augments nnU-Net with component-adaptive Tversky and MIL-based lesion supervision to raise Dice scores, small-lesion recall, and error control on the MSLesSeg dataset.
Dante is a new open-source backend for the Dafne ecosystem that implements configurable training from scratch, layer freezing, and channel-wise LoRA for medical image segmentation, with validation showing faster convergence and higher Dice scores in cross-domain MRI tasks.
citing papers explorer
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Camyla: Scaling Autonomous Research in Medical Image Segmentation
Camyla autonomously generates research proposals, experiments, and manuscripts in medical image segmentation, outperforming baselines on 24 of 31 recent datasets while producing 40 human-reviewed papers.
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SIAM: Head and Brain MRI Segmentation from Few High-Quality Templates via Synthetic Training
SIAM achieves state-of-the-art whole-head MRI segmentation of 16 structures including extra-cerebral tissues by training on synthetic data from just six manual templates, matching or exceeding prior methods on 301 scans across eight heterogeneous datasets.
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Component-Adaptive and Lesion-Level Supervision for Improved Small Structure Segmentation in Brain MRI
CATMIL augments nnU-Net with component-adaptive Tversky and MIL-based lesion supervision to raise Dice scores, small-lesion recall, and error control on the MSLesSeg dataset.
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Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation
Dante is a new open-source backend for the Dafne ecosystem that implements configurable training from scratch, layer freezing, and channel-wise LoRA for medical image segmentation, with validation showing faster convergence and higher Dice scores in cross-domain MRI tasks.