A literature-oriented view of why microscopy segmentation looks mature at the model level while portability, trust, and operational usability remain unresolved.
Writing
Writing
Research, project notes, methods, and technical notes that keep the work inspectable.
Research
Field-level analysis, literature synthesis, benchmark interpretation, and reliability framing that make the domain easier to reason about.
Microscopy already has real benchmarks, and some remain open today. The harder problem is that leaderboard rank often reflects a submitted pipeline under a narrow benchmark contract, while practitioners need evidence they can trust when choosing methods for real data.
Project Notes
Project-linked notes that keep decisions, tradeoffs, and changes in judgment close to the work itself.
A well-scoped dataset, a concrete supervised task, and a better foundation for disciplined learning than chasing a fragmented benchmark landscape.
A project note on making the BSCCM benchmark concrete: what the 23-input to 6-target task actually is, why it is a disciplined first scope, and why it came before more semantic or less directly assay-linked alternatives.
Methods
Reusable engineering patterns that make experiments steadier, cleaner, and easier to trust.
Why trustworthy ML experimentation starts with protected evaluation, simple baselines, controlled reruns, direct data inspection, and artifacts that preserve evidence.
A method for defining the comparison contract before optimization begins: split discipline, target definitions, metric interpretation, artifact review, and the checks that keep early results from becoming false confidence.
Why trustworthy ML training needs more than loss curves: stable metric semantics, structured logging, qualitative inspection, and run records that make behavior legible over time.
How Hydra, Pydantic, and PyTorch help make machine learning runs easier to define, validate, rerun, and audit.
Technical Notes
Focused concept notes for mechanisms, tradeoffs, and implementation details worth keeping close at hand.
A step-by-step explanation of the U-Net architecture, the problem pressures that shaped it, and why it remains a serious baseline for many dense prediction tasks.
How standard convolution combines spatial filtering and channel mixing, and why depthwise plus 1x1 convolutions split those jobs apart.
What BatchNorm and GroupNorm actually normalize inside CNNs, why that changes optimization behavior, and when batch-dependent statistics become a liability.
How squeeze-and-excitation uses global context to recalibrate channels, why the block stayed cheap enough to spread, and what later attention papers changed.
How initialization controls signal and gradient scale, why Xavier and Kaiming rules exist, and what later theory adds about criticality and Jacobian conditioning.
How encoder-decoder models separate representation learning from output construction, why compression and reconstruction stay in tension, and how the pattern evolved from autoencoders and U-Net to DETR, MAE, diffusion, and SAM.
Why stacked linear layers collapse into one linear transformation, how ReLU prevents that collapse, and why piecewise-linear gating gives deep networks expressive power.
How dense-prediction metrics change the object being compared, and why region, boundary, topology, instance, perceptual, and uncertainty errors need different evaluation lenses.