AI box for students & researchers
AI file storage, annotation, research, studying.

Strengths

Accessible interactive ML for bioimage analysis without coding. Sparse annotation (scribbles) lowers labeling burden. Supports segmentation, object classification, counting, and tracking across up to 5D data (3D + time + channels). Out-of-core, on-demand computation enables interactive prediction on datasets larger than RAM. Trained workflows can be exported and run headlessly via command line for reproducible batch processing.

Research Directions

• Active learning for interactive annotation: uncertainty-guided scribble suggestions to reduce labeling time.

• Self-supervised pretraining for 3D microscopy to reduce annotation burden.

• Hybrid deep feature integration: plug pretrained CNN/ViT features into ilastik classifier pipeline.

• Multi-resolution, coarse-to-fine inference for terabyte-scale datasets.

• Uncertainty-aware overlays to guide user corrections in real time.

• Systematic evaluation of annotation granularity (clicks vs scribbles vs outlines).

• Cross-dataset transfer and domain adaptation benchmarking.

• Standardized 5D interactive segmentation benchmark suite.

Concerns / Gaps

Limited exploration of model uncertainty calibration. Feature engineering remains largely hand-crafted. Generalization across modalities unclear. Human-in-the-loop efficiency not rigorously quantified. Scalability tradeoffs vs deep end-to-end models underexplored.

Last left off

Want to examine classifier type (random forest?) and feature stack in detail; compare to modern deep segmentation baselines and evaluate computational complexity scaling with dataset size.