Data & Analytics

Optimize per-class decision thresholds for macro-F1 by replacing the non-differentiable hard threshold with a sigmoid-sharpened soft-F1 surrogate and fitting the per-class threshold vector via least-squares — averaged over multiple random validation splits to suppress overfitting on rare classes

Double left-join on tracking data to create pairwise features (positions, velocities, distance) for both entities in an interaction pair

Compute Euclidean distance between entity pairs from tracking data and filter out pairs beyond a threshold to reduce inference candidates

Oversample multi-label images by giving each image a duplication multiplier equal to the max per-class multiplier among its labels, so every rare class gets repetition without exploding common-class counts — the standard fix for long-tail multi-label distributions where SMOTE / per-row oversampling doesn't apply

For multi-label classification, compute the per-class binary correlation matrix restricted to multi-label rows and the conditional class counts given a rare anchor class — reveals label groupings the model can exploit (shared classifier heads, hierarchical loss weighting, post-hoc consistency rules)

Generate count-regression training patches at a geometric pyramid of image scales (0.9^k) so one CNN handles within- and between-image object-size variation without explicit anchors

Route each DICOM series to a per-modality window-center / window-width pair (CT/CTA/MRA/MRI) before normalization, so the same model can ingest mixed modalities without one modality's intensity range washing out the others

Combines mixup augmentation (linear interpolation of image pairs and their labels) with label smoothing in a single training pipeline for regularization.

Mask the corners of a dermoscopy image with a random-radius black circle to mimic the dark vignette of a dermatoscope field of view

Lovasz hinge loss that directly optimizes IoU for binary segmentation by computing a convex surrogate via sorted prediction errors and cumulative Jaccard gradients.

Evaluates image-to-sequence models using mean Levenshtein edit distance between predicted and ground-truth strings.

Average predictions from K independently trained fold models at inference time for variance reduction without stacking complexity

Resample 3D CT volumes to uniform voxel spacing using scipy zoom, normalizing physical dimensions across scanners

Evaluation scorer that merges predictions with GT per frame, takes top-IoU match per GT, and computes weighted accuracy with IoU threshold gate

Apply radiological windowing to HU images — clamp to center/width range for tissue-specific visualization (lung, bone, soft tissue)

Inverts whole slide image pixel values (1 - x) so white background becomes zero, enabling standard zero-padding and making tissue regions the active signal.

Resolve overlapping instance masks by greedily assigning contested pixels to higher-confidence predictions using a running occupancy map

Unfreezes backbone layers for fine-tuning while keeping BatchNorm layers frozen to preserve pretrained running statistics.

Load EfficientDet pretrained on COCO with the original 90-class head, then swap in a fresh HeadNet with your own num_classes — keeps the BiFPN feature pyramid pretrained and only retrains the classification head, the canonical transfer-learning recipe for the effdet PyTorch port

Convert raw DICOM pixel arrays to Hounsfield Units using per-slice RescaleSlope/RescaleIntercept, with outside-scanner clamping

Normalize DICOM pixel values using frequency-equalized histogram bins for globally consistent non-linear intensity mapping

Partition a large dataset into N balanced shards using integer key modulo arithmetic for reproducible, class-interleaved splits across CSV files

Run DeepSort on per-frame detections then overwrite each track cluster's label with the most common mapped label across the track's lifetime

Add a cumulative sum channel along the vertical axis to capture directional structural trends in grayscale images for segmentation