Michal Kaszubski

Research

Work judged by scientific standards: patient-level splits, plain status wording, and results only where they exist.

Thesis bar chart of uncertainty-error AUROC, with error bars, for deep ensembles, MC dropout and test-time augmentation. ResNet-18 scores 0.64 to 0.69 and ResNet-50 0.60 to 0.65. The DenseNet-121 deep ensemble reaches only 0.52, just above the dashed chance line at 0.50.

MIQA prostate DWI

Research, 2025 to 2026

MSc research at UCL on automated detection of geometric distortion in prostate diffusion-weighted MRI, and on whether a model can tell when its own judgement is unreliable.

Role
MSc researcher, UCL (thesis author)
Built with
PyTorch, DenseNet-121, Deep ensembles, Selective prediction
Evidence
Research evidence
Result
Three-seed DenseNet-121 ensemble: mean AUROC 0.819, SD 0.049 across five patient-level folds, up to 0.17 AUROC over classical radiomic baselines. Retrospective triage simulation: 83.6% retained-case accuracy at 70% coverage (thesis, 2026).
Inspect the work

RSNA knee MRI challenge

Research, 2026

An entry to the 2026 RSNA Knee Abnormality Detection competition on Kaggle, now closed: radiology reports taught an EfficientNetV2 and DINOv2 ensemble to read knee MRI from the images alone. It reached 0.941 on the public leaderboard by itself.

Role
Competitor, solo: DICOM data engineering, weak-supervision pipeline, model design and training, submission engineering
Built with
PyTorch, EfficientNetV2, DINOv2, timm
Evidence
Public benchmark
Result
1,849th of 6,425 teams (private 0.942, public 0.958) from a blend built on the strongest public notebook. My own ensemble reached 0.941 on the public leaderboard alone, 1 September 2026. No medal.
Inspect the work