Skill Claude CodeCodex
Convert raw DICOM pixel arrays to Hounsfield Units using per-slice RescaleSlope/RescaleIntercept, with outside-scanner clamping.
Skill Claude CodeCodex
Convert raw DICOM pixel arrays to Hounsfield Units using per-slice RescaleSlope/RescaleIntercept, with outside-scanner clamping.
Skill Claude CodeCodex
Order DICOM slices into a coherent 3D volume by sorting on ImagePositionPatient[2] (the Z coordinate in patient space), with a filename-integer fallback for series whose tag is missing — never trust filename alphabetical order, never trust InstanceNumber.
Skill Claude CodeCodex
Decodes MRI scan plane (axial, coronal, sagittal) from DICOM ImageOrientationPatient direction cosine vectors.
Skill Claude CodeCodex
Read DICOM X-ray files with VOI LUT transformation and MONOCHROME1 inversion for correct pixel intensity rendering.
Skill Claude CodeCodex
Use a soft macro-F1 loss 1 − mean(2·tp / (2·tp + fp + fn)) computed from raw sigmoid probabilities (no thresholding) as a direct training objective for multi-label classification, optionally combined with BCE — closes the gap between training surrogate and the F1 metric the leaderboard scores.
Skill Claude CodeCodex
Recover (x, y, class) point labels from color-coded dot-annotation image pairs via absdiff + blackout masking + Laplacian-of-Gaussian blob detection + center-pixel RGB classification.
Skill Claude CodeCodex
Reshape dual-view stacked channels into doubled batch dimension for shared backbone, then concatenate with tabular features for classification.
Skill Claude CodeCodex
Load EfficientDet pretrained on COCO with the original 90-class head, then swap in a fresh HeadNet with your own numclasses — keeps the BiFPN feature pyramid pretrained and only retrains the classification head, the canonical transfer-learning recipe for the effdet PyTorch port.
Skill Claude CodeCodex
Tracks an Exponential Moving Average of model weights during training and evaluates both live and EMA models for more stable predictions.
Skill Claude CodeCodex
GPU-accelerated k-NN regression on CLIP image embeddings using cosine distance and inverse-distance-power weighting to predict target embedding vectors.
Skill Claude CodeCodex
Collect test predictions each epoch via callback and combine with exponentially increasing weights favoring later epochs.
Skill Claude CodeCodex
Aggregate per-slice predictions into exam-level labels that satisfy a competition's mutual-exclusion hierarchy (positive vs negative vs indeterminate), using a top-down rule cascade — first decide the exam class, then conditionally rescale the dependent labels so the submission stays internally consistent.
Skill Claude CodeCodex
Pad variable-length per-slice sequences to a fixed batch length, carry a 0/1 mask alongside, and multiply per-slice BCE by the mask before reducing — gives correct per-exam loss with batched training and zero contamination from padding tokens.
Skill Claude CodeCodex
Models multiple conditions with a single flat output layer of Nlabels × Nclasses logits, sliced into per-condition softmax at inference.
Skill Claude CodeCodex
Alpha-weighted focal loss that down-weights easy examples to focus training on hard, misclassified pixels in imbalanced segmentation tasks.
Skill Claude CodeCodex
Encode motion and velocity by computing per-channel pixel differences between consecutive frames instead of stacking raw frames for RL visual observations.
Skill Claude CodeCodex
Average per-frame sigmoid predictions across sampled video frames to produce a stable video-level classification probability.
Skill Claude CodeCodex
Unfreezes backbone layers for fine-tuning while keeping BatchNorm layers frozen to preserve pretrained running statistics.
Skill Claude CodeCodex
Generates 3D segmentation training targets by placing Gaussian spheres at annotated point coordinates.
Skill Claude CodeCodex
Replaces global average pooling with Generalized Mean (GeM) pooling, using a learnable or fixed exponent to emphasize high-activation regions.
Skill Claude CodeCodex
Clean noisy numeric strings from generative model output by removing invalid characters, fixing malformed floats, and handling multiple decimal points.
Skill Claude CodeCodex
Accumulates gradients over multiple mini-batches before stepping the optimizer, simulating larger effective batch sizes.
Skill Claude CodeCodex
Resolve overlapping instance masks by greedily assigning contested pixels to higher-confidence predictions using a running occupancy map.
Skill Claude CodeCodex
Overlay real hair PNGs (masked via threshold) onto dermoscopy images to simulate body-hair occlusion as a domain-specific augmentation.
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: