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Two-layer MLP probe for ReMAP-PET 3-way classification.
Compares linear vs non-linear probing on the same embeddings.
"""
from __future__ import annotations
import argparse, json
from pathlib import Path
import numpy as np
import pandas as pd
import torch
from sklearn.neural_network import MLPClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.metrics import balanced_accuracy_score, roc_auc_score, accuracy_score
from sklearn.pipeline import make_pipeline
from torch.utils.data import DataLoader
from pet_vlm_dataset import PETSUVRDataset, collate_pet_suvr
from train_pet_foundation import PETSUVRFoundationModel, build_encoder
def extract_embeddings(checkpoint, split_csv, output_size, device):
ckpt = torch.load(checkpoint, map_location="cpu", weights_only=False)
saved_args = argparse.Namespace(**ckpt.get("args", {}))
for name in ("backbone", "embed_dim", "freeze_encoder"):
if not hasattr(saved_args, name) or getattr(saved_args, name) is None:
setattr(saved_args, name, ckpt.get("args", {}).get(name))
embed_dim = getattr(saved_args, "embed_dim", None) or 256
backbone = getattr(saved_args, "backbone", "medicalnet")
freeze = getattr(saved_args, "freeze_encoder", True)
manifest = pd.read_csv(split_csv)
dataset = PETSUVRDataset(split_csv, output_size=output_size)
loader = DataLoader(dataset, batch_size=4, shuffle=False, num_workers=2, collate_fn=collate_pet_suvr)
encoder = build_encoder(saved_args)
model = PETSUVRFoundationModel(encoder, int(dataset[0]["suvr"].numel()), embed_dim, freeze).to(device)
model.load_state_dict(ckpt["model"], strict=True)
model.eval()
pet_embs, suvr_preds = [], []
with torch.no_grad():
for batch in loader:
image = batch["image"].to(device)
suvr = batch["suvr"].to(device)
pet_feat = model.pet_encoder(image)
pet_z = torch.nn.functional.normalize(model.pet_projector(pet_feat), dim=-1)
pet_embs.append(pet_z.cpu().numpy())
suvr_preds.append(model(image, suvr)["pred_suvr"].cpu().numpy())
pet_z = np.concatenate(pet_embs, axis=0)
pred_suvr = np.concatenate(suvr_preds, axis=0)
return manifest, pet_z, pred_suvr
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoint", type=Path, required=True)
parser.add_argument("--train", type=Path, default=Path("data/metadata/splits/train_clinical.csv"))
parser.add_argument("--val", type=Path, default=Path("data/metadata/splits/val_clinical.csv"))
parser.add_argument("--test", type=Path, default=Path("data/metadata/splits/test_clinical.csv"))
parser.add_argument("--output-size", type=int, nargs=3, default=(96, 96, 96))
args = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Loading checkpoint: {args.checkpoint}")
train_df, train_pet, train_suvr = extract_embeddings(args.checkpoint, args.train, tuple(args.output_size), device)
val_df, val_pet, val_suvr = extract_embeddings(args.checkpoint, args.val, tuple(args.output_size), device)
test_df, test_pet, test_suvr = extract_embeddings(args.checkpoint, args.test, tuple(args.output_size), device)
# Extract labels for 3-way classification
encoder = LabelEncoder()
all_labels = pd.concat([train_df["clinical_label"], val_df["clinical_label"], test_df["clinical_label"]])
encoder.fit(all_labels.astype(str))
y_train = encoder.transform(train_df["clinical_label"].astype(str))
y_val = encoder.transform(val_df["clinical_label"].astype(str))
y_test = encoder.transform(test_df["clinical_label"].astype(str))
n_classes = len(encoder.classes_)
# ---- Linear probe (baseline) ----
print("\n=== Linear Probe (Logistic Regression) ===")
best_bal, best_c = 0, 0.01
for c in [0.01, 0.03, 0.1, 0.3, 1.0, 3.0, 10.0, 30.0]:
pipe = make_pipeline(StandardScaler(), LogisticRegression(C=c, max_iter=5000, solver="lbfgs"))
pipe.fit(train_pet, y_train)
val_pred = pipe.predict(val_pet)
bal = balanced_accuracy_score(y_val, val_pred)
print(f" C={c:.2f} val_bal={bal:.4f}")
if bal > best_bal:
best_bal, best_c = bal, c
pipe = make_pipeline(StandardScaler(), LogisticRegression(C=best_c, max_iter=5000, solver="lbfgs"))
pipe.fit(train_pet, y_train)
test_pred = pipe.predict(test_pet)
test_prob = pipe.predict_proba(test_pet)
linear_bal = balanced_accuracy_score(y_test, test_pred)
linear_auroc = roc_auc_score(y_test, test_prob, multi_class="ovr", average="macro")
print(f" Best C={best_c}, Test BalAcc={linear_bal:.4f}, AUROC={linear_auroc:.4f}")
# ---- MLP probe ----
print("\n=== MLP Probe (2-layer) ===")
best_bal2, best_alpha = 0, 0.01
for alpha in [0.0001, 0.001, 0.01, 0.1, 1.0]:
for hidden in [64, 128, 256]:
pipe2 = make_pipeline(
StandardScaler(),
MLPClassifier(hidden_layer_sizes=(hidden, hidden // 2),
alpha=alpha, max_iter=2000,
early_stopping=True, validation_fraction=0.1,
random_state=42)
)
pipe2.fit(train_pet, y_train)
val_pred2 = pipe2.predict(val_pet)
bal2 = balanced_accuracy_score(y_val, val_pred2)
if bal2 > best_bal2:
best_bal2, best_alpha, best_hidden = bal2, alpha, hidden
print(f" Best alpha={best_alpha}, hidden={best_hidden}, val_bal={best_bal2:.4f}")
pipe2 = make_pipeline(
StandardScaler(),
MLPClassifier(hidden_layer_sizes=(best_hidden, best_hidden // 2),
alpha=best_alpha, max_iter=5000, random_state=42)
)
pipe2.fit(train_pet, y_train)
test_pred2 = pipe2.predict(test_pet)
test_prob2 = pipe2.predict_proba(test_pet)
mlp_bal = balanced_accuracy_score(y_test, test_pred2)
mlp_auroc = roc_auc_score(y_test, test_prob2, multi_class="ovr", average="macro")
print(f" Test BalAcc={mlp_bal:.4f}, AUROC={mlp_auroc:.4f}")
# ---- Combined features probe ----
print("\n=== Linear Probe (PET + Predicted SUVR) ===")
train_comb = np.concatenate([train_pet, train_suvr], axis=1)
val_comb = np.concatenate([val_pet, val_suvr], axis=1)
test_comb = np.concatenate([test_pet, test_suvr], axis=1)
best_bal3, best_c3 = 0, 0.01
for c in [0.01, 0.03, 0.1, 0.3, 1.0, 3.0, 10.0, 30.0]:
pipe3 = make_pipeline(StandardScaler(), LogisticRegression(C=c, max_iter=5000, solver="lbfgs"))
pipe3.fit(train_comb, y_train)
val_pred3 = pipe3.predict(val_comb)
bal3 = balanced_accuracy_score(y_val, val_pred3)
if bal3 > best_bal3:
best_bal3, best_c3 = bal3, c
pipe3 = make_pipeline(StandardScaler(), LogisticRegression(C=best_c3, max_iter=5000, solver="lbfgs"))
pipe3.fit(train_comb, y_train)
test_pred3 = pipe3.predict(test_comb)
test_prob3 = pipe3.predict_proba(test_comb)
comb_bal = balanced_accuracy_score(y_test, test_pred3)
comb_auroc = roc_auc_score(y_test, test_prob3, multi_class="ovr", average="macro")
print(f" Best C={best_c3}, Test BalAcc={comb_bal:.4f}, AUROC={comb_auroc:.4f}")
# Summary
print(f"\n=== Summary (3-way test AUROC) ===")
print(f" Linear probe (original): {linear_auroc:.4f}")
print(f" MLP probe (2-layer): {mlp_auroc:.4f}")
print(f" Linear probe (PET+SUVR): {comb_auroc:.4f}")
print(f" MedicalNet frozen baseline: 0.7567")
if __name__ == "__main__":
main()
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