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🎯 Authors present the first BraTS challenge on pediatric brain tumor segmentation using multi-institutional data, aimed at supporting clinical trial workflows and fostering collaboration in pediatric neuro-oncology. 🔎 Free to read:

🚨 New publication alert:.📢 “BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023.”.🖊️ @anahita_fathi, @NastranKhalili, X Liu, D Haldar,. , @adamcresnick, @SpyridonBakas, @NeuroradAI, M G Linguraru. ⬇️.

As a reminder, MELBA and MIDL have a formal strategic partnership, enabling journal-to-conference and conference-to-journal tracks that support the seamless dissemination of high-quality research. We’re proud to contribute to this collaborative and open-access ecosystem!.

🎙️ MELBA at MIDL 2025 🎙️. MELBA had the pleasure of hosting the final session of Day 2 at MIDL 2025, featuring two outstanding oral presentations. This session highlighted the strong synergy between MELBA and MIDL. ⬇️.

🎯 Authors show that transformer design choices from natural images often hurt performance in medical imaging. On mammography and chest CT, simpler architectures outperform complex ones, highlighting the value of simplicity. 🔎 Free to read:

🚨 New publication alert:.📢 “Understanding differences in applying DETR to natural and medical images.”.🖊️ Y Xu, Y Shen, C Fernandez-Granda, @heacockmd, @kjgeras. ��️.

🎯 Authors introduce BrainMorph, a keypoint-based foundation model for multi-modal brain MRI registration, trained on 100,000+ 3D scans and enabling robust, interpretable, and scalable alignment in challenging scenarios. 🔎 Free to read:

🚨 New publication alert:.📢 “BrainMorph: A Foundational Keypoint Model for Robust and Flexible Brain MRI Registration.”.🖊️ A Q Wang, @RachitSaluja, H Kim, X He, @AdrianDalca, @mertrory. ⬇️.

🎯 Authors propose a lightweight adaptation of SAM for medical imaging, trainable on a single A100 GPU. Using modality/content prompts and modality-based sampling, the model enhances segmentation with minimal computational cost. 🔎 Free to read:

🚨 New publication alert:.📢 “MCP-MedSAM: A Powerful Lightweight Medical Segment Anything Model Trained with a Single GPU in Just One Day.”.🖊️ D Lyu, R Gao, @MariusStaring. ⬇️.

🎯 Authors evaluate fine-tuning strategies for SAM in medical image segmentation, showing that parameter-efficient tuning and self-supervised pretraining improve performance, while network architecture has a limited effect. 🔎 Free to read:

🚨 New publication alert:.📢 “How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model.”.🖊️ H Gu, H Dong, J Yang, @MazurowskiPhD (@MazurowskiLab). ⬇️.

📢 MELBA is growing its editorial team!.We’re excited to welcome three new Associate Editors who bring outstanding expertise in machine learning and biomedical imaging. Welcome to:.🔹 @BernhardKainz1 .🔹 @MariaVakalopou1 .🔹 Lisa Koch. We’re thrilled to have you on board! 🚀.

Exciting News! 🎉 .We are thrilled to welcome @IsmailBenAyed1 as a new Executive Editor of MELBA’s Editorial Board. With a wealth of experience in #ComputerVision & #BiomedicalImaging, Dr. Ben Ayed will lead us to the next frontier of cutting-edge research. Stay tuned! 🚀.

🎯 Authors propose a soft-labeling method for segmentation that incorporates image intensity information via geodesic distance transforms to better capture spatial and class-wise relationships. 🔎 Free to read:

🚨 New publication alert:.📢 “GeoLS: an Intensity-based, Geodesic Soft Labeling for Image Segmentation.”.🖊️ @sukeshadiga, @josedolz_ets, H Lombaert. ⬇️.

🎯Authors propose prompting strategies to mitigate hallucinations and improve diagnostic accuracy in Medical Visual Question Answering using VLMs, by incorporating pathology explanations and weak learner outputs. 🔎 Free to read:

🚨 New publication alert:.📢 “Prompting Medical Large Vision-Language Models to Diagnose Pathologies by Visual Question Answering.”.🖊️ D Guo, D Terzopoulos. ⬇️.

🎯 Authors present the BraTS 2023 Intracranial Meningioma Segmentation Challenge and its results, introducing a large expert-annotated MRI dataset and state-of-the-art deep learning models while highlighting preprocessing challenges. 🔎 Free to read:

🚨 New publication alert:.📢 “Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge.”.🖊️ D LaBella, U Baid, O Khanna, S McBurney-Lin, …, J Villanueva-Meyer, A M Rauschecker, A Nada, M Aboian, A E Flanders, B Wiestler, S Bakas, E Calabrese. ⬇️.