medical-imaging

an archive of posts with this tag

May 27, 2026 sparse-view clinical reconstruction: explicit vs implicit representations
May 25, 2026 what i look for in a strong medical ai research problem
May 21, 2026 the gap between benchmark performance and clinical usefulness
May 20, 2026 why i keep returning to geometry in biomedical ai
May 18, 2026 a short guide to reading papers in surgical vision
May 15, 2026 what makes a research prototype different from a deployable clinical tool
May 14, 2026 reproducibility lessons from building multiple medical vision pipelines
May 13, 2026 how i structure ablations in small-data medical imaging projects
May 12, 2026 when to use cnns, transformers, or foundation models in medical imaging
May 11, 2026 neural active contours: why old geometry ideas still matter in deep segmentation
May 04, 2026 designing a reproducible medical imaging project instead of a one-off notebook
May 01, 2026 what i learned benchmarking skin lesion segmentation beyond u-net
Apr 30, 2026 turning segmentation masks into 3d anatomical surfaces for navigation workflows
Apr 29, 2026 how to think about physics-informed learning without the hype
Apr 27, 2026 what makes surgical computer vision different from standard vision benchmarks
Apr 07, 2026 evaluation traps in biomedical ai: metrics that look good but say little
Mar 15, 2026 neural anisotropic diffusion: unrolling a pde for medical image denoising
Feb 06, 2026 building a lung ct pipeline with monai, simpleitk, and vtk
Jan 15, 2026 few-shot 2d echo to 3d cardiac reconstruction: what actually makes it hard
Dec 27, 2025 why deformation is the hard problem in image-guided robotic surgery
Dec 14, 2025 how i moved from biomedical engineering into robotic surgery research
Nov 26, 2025 from medical image segmentation to usable geometry: why meshes matter
Nov 03, 2025 what course projects taught me that research papers did not
Oct 22, 2025 what sparse-view clinical reconstruction teaches you about real-world 3d vision