{
  "model_type": "ResEncLUNet",
  "task": "binary 3D segmentation of amyloid-beta plaques in light-sheet microscopy (all LANTERN Abeta cohorts)",
  "framework": "pytorch",
  "num_input_channels": 1,
  "num_classes": 2,
  "encoder": {
    "name": "ResEncL",
    "n_stages": 6,
    "features_per_stage": [
      32,
      64,
      128,
      256,
      320,
      320
    ],
    "frozen": true,
    "init": "nnBYOL3D self-supervised pretraining on ~50k near-iso-4 light-sheet patches"
  },
  "params_total": 102462826,
  "params_trainable": 12166250,
  "input": {
    "patch_size": [
      128,
      128,
      128
    ],
    "axes_order": "ZYX",
    "voxel_size_um": {
      "z": "2.75-4.4 (cohort dependent)",
      "y": 3.25,
      "x": 3.25
    },
    "dtype": "float32",
    "note": "raw intensities; NOT N4-corrected or rescaled"
  },
  "preprocessing": {
    "method": "per-tile percentile clip then z-score",
    "clip_percentiles": [
      0.5,
      99.5
    ],
    "order": "clip first, then mean/std computed on the clipped volume"
  },
  "inference": {
    "n_folds": 5,
    "combine": "sum of per-fold plaque decisions (softmax[1] >= 0.5); plaque where votes >= 3",
    "prob_threshold": 0.5,
    "tiling_stride": 64,
    "tile_overlap_combine": "mean of per-fold softmax over overlapping tiles (not max)",
    "recommended_precision": "bf16",
    "recommended_batch_size": 8,
    "benchmark_ms_per_tile_L40S": 27.8,
    "recommended_postprocess": "drop predicted plaque components < 8 near-iso voxels"
  },
  "training": {
    "n_patches": 178,
    "n_subjects": 68,
    "set": "v7/total_set/v1",
    "composition": {
      "ki3": 74,
      "o22b": 4,
      "mc4": 53,
      "prune": 4,
      "vac": 3,
      "mapt": 40
    },
    "empty_plaque_patches": 19,
    "patches_with_label2_folded_to_background": 25,
    "cv": "5-fold, random patch-level split (NOT subject-grouped)",
    "split_seed": 1234,
    "gt_min_connected_component_voxels": 4,
    "label_mode": "plaque",
    "loss": "DiceCE (batch-Dice on plaque)",
    "decoder_init": "random (no warm start)",
    "encoder": "frozen SSL ResEncL (byol_c128)",
    "epochs": 250,
    "optimizer": "AdamW",
    "lr": 0.001,
    "warmup_epochs": 10,
    "schedule": "cosine",
    "batch_size": 2,
    "accumulate_grad_batches": 4,
    "precision": "bf16-mixed",
    "checkpoint": "best-val object F1"
  },
  "evaluation": {
    "primary_metric": "object F1 @ IoU>=0.5, 26-connectivity, per-fold validation split (36 patches), mean over 5 folds at each fold's best epoch",
    "object_f1": 0.4589,
    "object_f1_sd_over_folds": 0.06,
    "precision": 0.426,
    "recall": 0.501,
    "fp_per_patch": 100.7,
    "object_f1_with_pred_min_cc_8": 0.5042,
    "fp_per_patch_with_pred_min_cc_8": 70.0,
    "voxel_dice": 0.605,
    "per_fold": [
      {
        "fp_per_patch": 108.88888549804688,
        "fp_per_patch_predfilt": 74.02777862548828,
        "loss": 0.3341991901397705,
        "object_f1": 0.4092409312725067,
        "object_f1_predfilt": 0.4598941504955292,
        "object_precision": 0.36280885338783264,
        "object_recall": 0.4693019390106201,
        "voxel_dice": 0.6610821485519409,
        "epoch": 99
      },
      {
        "fp_per_patch": 81.22222137451172,
        "fp_per_patch_predfilt": 58.83333206176758,
        "loss": 0.3775239586830139,
        "object_f1": 0.4456420838832855,
        "object_f1_predfilt": 0.4795815646648407,
        "object_precision": 0.45058247447013855,
        "object_recall": 0.44080883264541626,
        "voxel_dice": 0.7066648602485657,
        "epoch": 64
      },
      {
        "fp_per_patch": 99.97222137451172,
        "fp_per_patch_predfilt": 66.44444274902344,
        "loss": 0.2981194853782654,
        "object_f1": 0.5641064643859863,
        "object_f1_predfilt": 0.6198633313179016,
        "object_precision": 0.5060389637947083,
        "object_recall": 0.6372277736663818,
        "voxel_dice": 0.6496661901473999,
        "epoch": 119
      },
      {
        "fp_per_patch": 122.68571472167969,
        "fp_per_patch_predfilt": 88.71428680419922,
        "loss": 0.49983668327331543,
        "object_f1": 0.397587388753891,
        "object_f1_predfilt": 0.4390422999858856,
        "object_precision": 0.3591044843196869,
        "object_recall": 0.4453081488609314,
        "voxel_dice": 0.5200831294059753,
        "epoch": 109
      },
      {
        "fp_per_patch": 90.97142791748047,
        "fp_per_patch_predfilt": 61.88571548461914,
        "loss": 0.5084365010261536,
        "object_f1": 0.47774097323417664,
        "object_f1_predfilt": 0.5225977301597595,
        "object_precision": 0.44923022389411926,
        "object_recall": 0.5101158618927002,
        "voxel_dice": 0.4864481985569,
        "epoch": 24
      }
    ],
    "note": "patch-level CV with same-subject leakage; validation metric at the selected (best) epoch, so optimistic; LR 3e-4/1e-4 within 0.02; GT floor 8 variant +0.02 raw F1 / +0.05 pred-filtered F1 at identical voxel Dice"
  },
  "license": "cc-by-nc-4.0",
  "code": "https://github.com/Arshya-Guru/LANTERN",
  "classes": [
    "background",
    "plaque"
  ],
  "hub_repo": "apooladi/lantern-abeta",
  "arm": "scratchdec_mincc4_cond",
  "weights": "best-val",
  "bottleneck_conditioner": {
    "enabled": true,
    "inputs": [
      "log1p(mean of raw tile)",
      "log1p(std of raw tile)"
    ],
    "where": "bottleneck (deepest encoder features), Linear(2->32) broadcast + 1x1x1 conv mix",
    "standardisation": "per-fold buffers in state_dict (cond.log_mean_mu, cond.log_mean_sd, cond.log_std_mu, cond.log_std_sd)",
    "train_dropout": 0.3,
    "required_at_inference": true,
    "forward_signature": "net(x, raw_mean=torch.tensor([[m]]), raw_std=torch.tensor([[s]]))  # m, s over the RAW (unnormalised) 128^3 tile"
  }
}