Mohammed Baharoon

Thrilled to share that our paper, “Exploring the Design Space of 3D MLLMs for CT Report Generation,” has been accepted to @MICCAI_Society 2025! 🎉 This work—developed during Mohammed Baharoon’s internship at the Vector Institute with Jun Ma (@junma)—secured 2nd place in the MICCAI 2024 AMOS-MM Challenge 🥈. We systematically explored how to build effective 3D multimodal large language models (MLLMs) for CT report generation, focusing on: --Architectures: visual encoders, projectors, and language models --Training strategies: from frozen LLMs to parameter-efficient fine-tuning --Report completeness: knowledge-driven methods to enrich outputs 💡 Key innovations: --Binary-based Questioning (BQ): prompts the model to ask yes/no questions about common findings, reducing missed abnormalities. --Naive Normality (NN): automatically inserts “normal” statements for unmentioned organs, improving clarity and completeness. Together, these boosted the GREEN score by up to 10%, showing how simple post-processing can make AI-generated reports more clinically accurate. Takeaways: 1. Architecture matters more than LLM size 2. Higher resolution isn’t always better if pre-training and fine-tuning resolutions mismatch 📄 Full paper: papers.miccai.org/miccai-202… 💻 Code: github.com/bowang-lab/AMOS-M… Shoutout to Mohammed Baharoon and Jun Ma for their leadership in this project! Also thanks to all other co-authors, Congyu Fang and Augustin Toma, for their tremendous contributions! 🙏 @VectorInst @UHN_Research @UHNAIHUB @UofTCompSci

Ready for the most powerful foundation model for medical images/videos? 🚨 Just dropped: MedSAM2 The next-gen foundation model for 3D medical image & video segmentation — built on top of SAM 2.1. • Project site: medsam2.github.io • Paper: arxiv.org/abs/2504.03600 Why it matters: • Trained on 455K+ 3D image–mask pairs & 76K+ annotated video frames • >85% reduction in human annotation costs (validated in 3 studies) • Fast, accurate, and generalizes across organs, modalities, and pathologies Big impact: We used MedSAM2 to create 3 massive datasets: • 5,000 CT lesions • 3,984 liver MRI lesions • 251,550 echo video frames Plug & play: Deployable in: → 3D Slicer → JupyterLab → Gradio → Google Colab We open-sourced everything! Explore more: • Models: huggingface.co/wanglab/MedSA… • Datasets:  → CT: huggingface.co/datasets/wang…  → MRI: huggingface.co/datasets/wang… • 3D Slicer plugin: github.com/bowang-lab/MedSAM… • Segmentation dataset hub: medsam-datasetlist.github.io Let’s collaborate: Have unlabeled 3D medical images or videos? We’re actively seeking partners to co-create new public datasets. DM us or connect! #MedSAM2 #MedicalAI #ComputerVision #3Dsegmentation #FoundationModels #AIforHealth Also shoutout to @JunMa_11, the machine learning lead at our @UHNAIHUB for his amazing leadership in this project!