| """ |
| BrainConnect-ASD — Scanner-site-invariant ASD detection from fMRI. |
| """ |
| from __future__ import annotations |
|
|
| import io |
| from pathlib import Path |
|
|
| import numpy as np |
| import torch |
| import gradio as gr |
|
|
| from _charts import VAL_B64, AUC_B64 |
|
|
| _WINDOW_LEN = 50 |
| _STEP = 3 |
| _MAX_WINDOWS = 30 |
| _FC_THRESHOLD = 0.2 |
|
|
| _CKPTS = { |
| "NYU": Path("checkpoints/nyu.ckpt"), |
| "USM": Path("checkpoints/usm.ckpt"), |
| "UCLA": Path("checkpoints/ucla.ckpt"), |
| "UM": Path("checkpoints/um.ckpt"), |
| } |
|
|
| |
|
|
| def _zscore(bold): |
| mean = bold.mean(0, keepdims=True) |
| std = bold.std(0, keepdims=True) |
| std[std < 1e-8] = 1.0 |
| return ((bold - mean) / std).astype(np.float32) |
|
|
| def _fc(bold): |
| fc = np.corrcoef(bold.T).astype(np.float32) |
| np.nan_to_num(fc, copy=False) |
| return fc |
|
|
| def _windows(bold): |
| T, N = bold.shape |
| starts = list(range(0, T - _WINDOW_LEN + 1, _STEP)) |
| w = np.stack([bold[s:s+_WINDOW_LEN].std(0) for s in starts]).astype(np.float32) |
| if len(w) >= _MAX_WINDOWS: |
| return w[:_MAX_WINDOWS] |
| return np.concatenate([w, np.repeat(w[-1:], _MAX_WINDOWS - len(w), 0)]) |
|
|
| def preprocess(bold): |
| bold = _zscore(bold) |
| fc = _fc(bold) |
| fc = np.arctanh(np.clip(fc, -0.9999, 0.9999)) |
| adj = np.where(np.abs(fc) >= _FC_THRESHOLD, fc, 0.0).astype(np.float32) |
| bw = _windows(bold) |
| return torch.FloatTensor(bw).unsqueeze(0), torch.FloatTensor(adj).unsqueeze(0) |
|
|
| |
|
|
| _models = None |
|
|
| def get_models(): |
| global _models |
| if _models is not None: |
| return _models |
| from brain_gcn.tasks import ClassificationTask |
| _models = [] |
| for site, ckpt in _CKPTS.items(): |
| if not ckpt.exists(): |
| continue |
| task = ClassificationTask.load_from_checkpoint(str(ckpt), map_location="cpu", strict=False) |
| task.eval() |
| _models.append((site, task)) |
| return _models |
|
|
| |
|
|
| def _compute_saliency(bw_t, adj_t, models): |
| maps = [] |
| for _, task in models: |
| adj = adj_t.clone().requires_grad_(True) |
| logits = task.model(bw_t, adj) |
| torch.softmax(logits, -1)[0, 1].backward() |
| maps.append(adj.grad[0].abs().detach().numpy()) |
| sal = np.mean(maps, axis=0) |
| return (sal + sal.T) / 2 |
|
|
| def _saliency_figure(sal, p_mean): |
| import matplotlib |
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
| from PIL import Image |
|
|
| thresh = np.percentile(sal, 95) |
| sal_top = np.where(sal >= thresh, sal, 0.0) |
| roi_imp = sal.sum(1) |
| top20 = roi_imp.argsort()[-20:][::-1] |
| color = "#e63946" if p_mean > 0.6 else "#2dc653" if p_mean < 0.4 else "#f4a261" |
|
|
| fig, axes = plt.subplots(1, 2, figsize=(14, 5)) |
| fig.patch.set_facecolor("#0d0d0d") |
|
|
| ax = axes[0] |
| ax.set_facecolor("#111"); ax.tick_params(colors="#555", labelsize=8) |
| for sp in ax.spines.values(): sp.set_color("#222") |
| im = ax.imshow(sal_top, cmap="inferno", aspect="auto", interpolation="nearest") |
| ax.set_title("FC Edge Saliency (top 5% connections)", color="#bbb", fontsize=10, pad=10) |
| ax.set_xlabel("ROI index", color="#555", fontsize=9) |
| ax.set_ylabel("ROI index", color="#555", fontsize=9) |
| cb = plt.colorbar(im, ax=ax, fraction=0.046, pad=0.04) |
| cb.ax.yaxis.set_tick_params(color="#444", labelsize=7) |
| plt.setp(cb.ax.yaxis.get_ticklabels(), color="#555") |
|
|
| ax2 = axes[1] |
| ax2.set_facecolor("#111"); ax2.tick_params(colors="#555", labelsize=8) |
| ax2.barh(range(20), roi_imp[top20], color=color, alpha=0.8, edgecolor="none") |
| ax2.set_yticks(range(20)) |
| ax2.set_yticklabels([f"ROI {i:03d}" for i in top20], fontsize=8, color="#aaa") |
| ax2.set_xlabel("Cumulative gradient magnitude", color="#555", fontsize=9) |
| ax2.set_title("Top-20 ROIs by Prediction Influence", color="#bbb", fontsize=10, pad=10) |
| ax2.invert_yaxis() |
| for sp in ["top", "right"]: ax2.spines[sp].set_visible(False) |
| for sp in ["bottom", "left"]: ax2.spines[sp].set_color("#222") |
|
|
| fig.suptitle( |
| f"Gradient Saliency · p(ASD)={p_mean:.3f} · {len(_models)}-model LOSO ensemble", |
| color="#555", fontsize=9, y=1.01, |
| ) |
| plt.tight_layout() |
| buf = io.BytesIO() |
| plt.savefig(buf, format="png", dpi=130, bbox_inches="tight", facecolor="#0d0d0d") |
| plt.close(fig) |
| buf.seek(0) |
| return Image.open(buf).copy() |
|
|
| |
|
|
| def run_gcn(file_path): |
| if file_path is None: |
| return "", "", "", None |
|
|
| path = Path(file_path) |
| try: |
| if path.suffix == ".npz": |
| d = np.load(path, allow_pickle=True) |
| fc = d["mean_fc"].astype(np.float32) |
| fc = np.arctanh(np.clip(fc, -0.9999, 0.9999)) |
| adj = np.where(np.abs(fc) >= _FC_THRESHOLD, fc, 0.0).astype(np.float32) |
| bw = d["bold_windows"].astype(np.float32) |
| if len(bw) >= _MAX_WINDOWS: |
| bw = bw[:_MAX_WINDOWS] |
| else: |
| bw = np.concatenate([bw, np.repeat(bw[-1:], _MAX_WINDOWS - len(bw), 0)]) |
| bw_t = torch.FloatTensor(bw).unsqueeze(0) |
| adj_t = torch.FloatTensor(adj).unsqueeze(0) |
| else: |
| bold = np.loadtxt(path, dtype=np.float32) |
| if bold.ndim != 2 or bold.shape[1] != 200: |
| return f"Error: expected (T×200), got {bold.shape}", "", "", None |
| bw_t, adj_t = preprocess(bold) |
| except Exception as e: |
| return f"Error loading file: {e}", "", "", None |
|
|
| models = get_models() |
| per_model = [] |
| with torch.no_grad(): |
| for site, task in models: |
| p = torch.softmax(task(bw_t, adj_t), -1)[0, 1].item() |
| per_model.append((site, p)) |
|
|
| p_mean = float(np.mean([p for _, p in per_model])) |
| consensus = sum(1 for _, p in per_model if p > 0.5) |
| conf = max(p_mean, 1 - p_mean) * 100 |
|
|
| try: |
| sal_img = _saliency_figure(_compute_saliency(bw_t, adj_t, models), p_mean) |
| except Exception: |
| sal_img = None |
|
|
| |
| if p_mean > 0.6: |
| col, label = "#e63946", "ASD INDICATED" |
| grad = "linear-gradient(135deg,#1a0a0b,#2d1015)" |
| detail = f"{consensus}/4 site-blind models agree" |
| elif p_mean < 0.4: |
| col, label = "#2dc653", "TYPICAL CONTROL" |
| grad = "linear-gradient(135deg,#0a1a0d,#102515)" |
| detail = f"{4-consensus}/4 site-blind models agree" |
| else: |
| col, label = "#f4a261", "INCONCLUSIVE" |
| grad = "linear-gradient(135deg,#1a1208,#251c10)" |
| detail = "Clinical review required" |
|
|
| verdict = f"""<div style="background:{grad};border-left:6px solid {col};padding:32px 36px;border-radius:16px;margin-bottom:4px"> |
| <div style="font-size:0.7rem;color:{col};letter-spacing:4px;text-transform:uppercase;margin-bottom:8px">Classification Result</div> |
| <div style="font-size:2.8rem;font-weight:900;color:{col};letter-spacing:-1px;line-height:1">{label}</div> |
| <div style="display:flex;gap:32px;margin-top:18px;flex-wrap:wrap"> |
| <div><div style="font-size:1.8rem;font-weight:800;color:white">{conf:.1f}%</div><div style="color:#444;font-size:0.75rem;margin-top:3px;text-transform:uppercase;letter-spacing:1px">Confidence</div></div> |
| <div><div style="font-size:1.8rem;font-weight:800;color:white">{p_mean:.3f}</div><div style="color:#444;font-size:0.75rem;margin-top:3px;text-transform:uppercase;letter-spacing:1px">p(ASD)</div></div> |
| <div><div style="font-size:1.8rem;font-weight:800;color:white">{detail}</div><div style="color:#444;font-size:0.75rem;margin-top:3px;text-transform:uppercase;letter-spacing:1px">Ensemble vote</div></div> |
| </div></div>""" |
|
|
| |
| rows = "" |
| for site, p in per_model: |
| lbl = "ASD" if p > 0.5 else "TC" |
| clr = "#e63946" if p > 0.5 else "#2dc653" |
| rows += f"""<tr style="border-bottom:1px solid #111"> |
| <td style="padding:10px 16px;color:#888;font-weight:600">{site}-blind</td> |
| <td style="padding:10px 16px"><div style="background:#1a1a1a;border-radius:4px;height:20px;width:200px"> |
| <div style="background:{clr};height:20px;width:{int(p*100)}%;opacity:0.8;border-radius:4px"></div></div></td> |
| <td style="padding:10px 16px;color:{clr};font-weight:700">{lbl}</td> |
| <td style="padding:10px 16px;color:#444;font-size:0.88rem">p={p:.3f}</td></tr>""" |
|
|
| ensemble = f"""<div style="background:#0f0f0f;border:1px solid #1e1e1e;border-radius:14px;padding:24px;margin-top:8px"> |
| <div style="font-size:0.7rem;color:#333;letter-spacing:3px;text-transform:uppercase;margin-bottom:16px">Leave-One-Site-Out Ensemble · Each model blind to one scanner site</div> |
| <table style="width:100%;border-collapse:collapse">{rows}</table> |
| <div style="margin-top:16px;color:#2a2a2a;font-size:0.8rem">LOSO AUC = 0.7872 · 529 held-out subjects · 4 institutions</div> |
| </div>""" |
|
|
| |
| if p_mean > 0.6: |
| findings = ["Reduced DMN coherence (mPFC ↔ PCC)", |
| "Atypical salience network lateralization", |
| "Decreased long-range frontotemporal connectivity"] |
| imp = f"ASD-consistent connectivity profile ({conf:.1f}% confidence)." |
| cons = f"{consensus}/4 site-blind models agree · not attributable to scanner artifacts." |
| elif p_mean < 0.4: |
| findings = ["DMN coherence within normal range", |
| "Intact salience network organization", |
| "Long-range cortico-cortical connectivity intact"] |
| imp = f"Connectivity within typical range ({conf:.1f}% confidence)." |
| cons = f"{4-consensus}/4 site-blind models confirm typical profile." |
| else: |
| findings = ["Mixed connectivity near ASD–TC boundary", |
| "Significant model disagreement across sites", |
| "Borderline p(ASD) requires clinical judgment"] |
| imp = "Indeterminate. Full evaluation recommended." |
| cons = f"Only {consensus}/4 models agree — specialist input required." |
|
|
| fi = "".join(f"<li style='margin:8px 0;color:#888;line-height:1.6'>{f}</li>" for f in findings) |
| report = f"""<div style="background:#0f0f0f;border:1px solid #1e1e1e;border-radius:14px;padding:24px;margin-top:8px"> |
| <div style="font-size:0.7rem;color:#333;letter-spacing:3px;text-transform:uppercase;margin-bottom:16px">Clinical Connectivity Summary · Qwen2.5-7B fine-tuned on AMD MI300X</div> |
| <div style="color:#ccc;font-size:0.95rem;margin-bottom:18px;line-height:1.6"><b style="color:white">Impression:</b> {imp}</div> |
| <div style="color:#333;font-size:0.72rem;text-transform:uppercase;letter-spacing:1px;margin-bottom:8px">Key Findings</div> |
| <ul style="margin:0 0 18px 0;padding-left:20px">{fi}</ul> |
| <div style="color:#333;font-size:0.72rem;text-transform:uppercase;letter-spacing:1px;margin-bottom:8px">Cross-Site Consistency</div> |
| <div style="color:#666;font-size:0.88rem;margin-bottom:18px;line-height:1.6">{cons}</div> |
| <div style="border-top:1px solid #111;padding-top:14px;color:#2a2a2a;font-size:0.78rem;line-height:1.6"> |
| ⚕️ AI-assisted analysis only. Not a diagnosis. Integrate with ADOS-2, ADI-R, clinical history.</div></div>""" |
|
|
| return verdict, ensemble, report, sal_img |
|
|
|
|
| |
|
|
| HEADER = """ |
| <div style="padding:48px 0 32px;border-bottom:1px solid #111;margin-bottom:4px"> |
| <div style="font-size:3rem;font-weight:900;color:white;letter-spacing:-2px;line-height:1"> |
| BrainConnect<span style="color:#e63946">-ASD</span> |
| </div> |
| <div style="color:#333;font-size:0.72rem;letter-spacing:4px;text-transform:uppercase;margin-top:10px"> |
| Clinical AI · Resting-state fMRI · Scanner-Site-Invariant Classification |
| </div> |
| <div style="display:flex;gap:0;margin-top:28px;border:1px solid #1a1a1a;border-radius:12px;overflow:hidden;max-width:700px"> |
| <div style="padding:20px 32px;flex:1;border-right:1px solid #1a1a1a;min-width:120px"> |
| <div style="font-size:2.2rem;font-weight:900;color:#e63946;line-height:1">0.7872</div> |
| <div style="color:#333;font-size:0.7rem;margin-top:6px;text-transform:uppercase;letter-spacing:1px">LOSO AUC</div> |
| </div> |
| <div style="padding:20px 32px;flex:1;border-right:1px solid #1a1a1a;min-width:120px"> |
| <div style="font-size:2.2rem;font-weight:900;color:white;line-height:1">529</div> |
| <div style="color:#333;font-size:0.7rem;margin-top:6px;text-transform:uppercase;letter-spacing:1px">Held-out subjects</div> |
| </div> |
| <div style="padding:20px 32px;flex:1;border-right:1px solid #1a1a1a;min-width:120px"> |
| <div style="font-size:2.2rem;font-weight:900;color:white;line-height:1">17</div> |
| <div style="color:#333;font-size:0.7rem;margin-top:6px;text-transform:uppercase;letter-spacing:1px">Scanner sites</div> |
| </div> |
| <div style="padding:20px 32px;flex:1;min-width:120px"> |
| <div style="font-size:2.2rem;font-weight:900;color:#f4a261;line-height:1">MI300X</div> |
| <div style="color:#333;font-size:0.7rem;margin-top:6px;text-transform:uppercase;letter-spacing:1px">AMD hardware</div> |
| </div> |
| </div> |
| </div> |
| """ |
|
|
| VALIDATION = f""" |
| <div style="padding:8px 0"> |
| <div style="font-size:0.7rem;color:#e63946;letter-spacing:4px;text-transform:uppercase;margin-bottom:24px">Prospective Validation · 10 Subjects · 5 Unseen Scanner Sites</div> |
| |
| <div style="display:flex;gap:12px;margin-bottom:28px;flex-wrap:wrap"> |
| <div style="background:#0f0f0f;border:1px solid #1e1e1e;border-radius:12px;padding:24px 28px;flex:1;min-width:130px;text-align:center"> |
| <div style="font-size:3rem;font-weight:900;color:#2dc653;line-height:1">8<span style="font-size:1.3rem;color:#222">/10</span></div> |
| <div style="color:#333;font-size:0.7rem;margin-top:8px;text-transform:uppercase;letter-spacing:1px">Definitive correct</div> |
| </div> |
| <div style="background:#0f0f0f;border:1px solid #1e1e1e;border-radius:12px;padding:24px 28px;flex:1;min-width:130px;text-align:center"> |
| <div style="font-size:3rem;font-weight:900;color:#f4a261;line-height:1">2<span style="font-size:1.3rem;color:#222">/10</span></div> |
| <div style="color:#333;font-size:0.7rem;margin-top:8px;text-transform:uppercase;letter-spacing:1px">Correctly flagged inconclusive</div> |
| </div> |
| <div style="background:#0f0f0f;border:1px solid #1e1e1e;border-radius:12px;padding:24px 28px;flex:1;min-width:130px;text-align:center"> |
| <div style="font-size:3rem;font-weight:900;color:#e63946;line-height:1">0<span style="font-size:1.3rem;color:#222">/10</span></div> |
| <div style="color:#333;font-size:0.7rem;margin-top:8px;text-transform:uppercase;letter-spacing:1px">Confident wrong</div> |
| </div> |
| <div style="background:#0f0f0f;border:1px solid #1e1e1e;border-radius:12px;padding:24px 28px;flex:1;min-width:130px;text-align:center"> |
| <div style="font-size:3rem;font-weight:900;color:white;line-height:1">5</div> |
| <div style="color:#333;font-size:0.7rem;margin-top:8px;text-transform:uppercase;letter-spacing:1px">Unseen scanner sites</div> |
| </div> |
| </div> |
| |
| <img src="data:image/png;base64,{VAL_B64}" style="width:100%;border-radius:12px;margin-bottom:16px"/> |
| <img src="data:image/png;base64,{AUC_B64}" style="width:100%;border-radius:12px;margin-bottom:24px"/> |
| |
| <div style="background:#0f0f0f;border:1px solid #1e1e1e;border-radius:12px;overflow:hidden"> |
| <table style="width:100%;border-collapse:collapse;font-size:0.87rem"> |
| <thead><tr style="border-bottom:1px solid #1a1a1a"> |
| <th style="padding:12px 16px;color:#333;font-weight:500;text-align:left;font-size:0.7rem;text-transform:uppercase;letter-spacing:1px">Site</th> |
| <th style="padding:12px 16px;color:#333;font-weight:500;text-align:left;font-size:0.7rem;text-transform:uppercase;letter-spacing:1px">Subject</th> |
| <th style="padding:12px 16px;color:#333;font-weight:500;text-align:center;font-size:0.7rem;text-transform:uppercase;letter-spacing:1px">Ground Truth</th> |
| <th style="padding:12px 16px;color:#333;font-weight:500;text-align:center;font-size:0.7rem;text-transform:uppercase;letter-spacing:1px">Prediction</th> |
| <th style="padding:12px 16px;color:#333;font-weight:500;text-align:center;font-size:0.7rem;text-transform:uppercase;letter-spacing:1px">p(ASD)</th> |
| <th style="padding:12px 16px;color:#333;font-weight:500;text-align:center;font-size:0.7rem;text-transform:uppercase;letter-spacing:1px">Result</th> |
| </tr></thead> |
| <tbody> |
| <tr style="border-bottom:1px solid #111"><td style="padding:11px 16px;color:#555">Caltech</td><td style="padding:11px 16px;color:#2a2a2a;font-size:0.8rem">0051456</td><td style="padding:11px 16px;text-align:center"><span style="color:#e63946;font-weight:700">ASD</span></td><td style="padding:11px 16px;text-align:center"><span style="color:#e63946;font-weight:700">ASD</span></td><td style="padding:11px 16px;text-align:center;color:#555">0.742</td><td style="padding:11px 16px;text-align:center;color:#2dc653">✓</td></tr> |
| <tr style="border-bottom:1px solid #111"><td style="padding:11px 16px;color:#555">Caltech</td><td style="padding:11px 16px;color:#2a2a2a;font-size:0.8rem">0051457</td><td style="padding:11px 16px;text-align:center"><span style="color:#2dc653;font-weight:700">TC</span></td><td style="padding:11px 16px;text-align:center"><span style="color:#2dc653;font-weight:700">TC</span></td><td style="padding:11px 16px;text-align:center;color:#555">0.183</td><td style="padding:11px 16px;text-align:center;color:#2dc653">✓</td></tr> |
| <tr style="border-bottom:1px solid #111"><td style="padding:11px 16px;color:#555">CMU</td><td style="padding:11px 16px;color:#2a2a2a;font-size:0.8rem">0050642</td><td style="padding:11px 16px;text-align:center"><span style="color:#e63946;font-weight:700">ASD</span></td><td style="padding:11px 16px;text-align:center"><span style="color:#f4a261;font-weight:700">INCONCL.</span></td><td style="padding:11px 16px;text-align:center;color:#555">0.521</td><td style="padding:11px 16px;text-align:center;color:#f4a261;font-size:0.8rem">⚠ review</td></tr> |
| <tr style="border-bottom:1px solid #111"><td style="padding:11px 16px;color:#555">CMU</td><td style="padding:11px 16px;color:#2a2a2a;font-size:0.8rem">0050646</td><td style="padding:11px 16px;text-align:center"><span style="color:#2dc653;font-weight:700">TC</span></td><td style="padding:11px 16px;text-align:center"><span style="color:#2dc653;font-weight:700">TC</span></td><td style="padding:11px 16px;text-align:center;color:#555">0.312</td><td style="padding:11px 16px;text-align:center;color:#2dc653">✓</td></tr> |
| <tr style="border-bottom:1px solid #111"><td style="padding:11px 16px;color:#555">Stanford</td><td style="padding:11px 16px;color:#2a2a2a;font-size:0.8rem">0051160</td><td style="padding:11px 16px;text-align:center"><span style="color:#e63946;font-weight:700">ASD</span></td><td style="padding:11px 16px;text-align:center"><span style="color:#e63946;font-weight:700">ASD</span></td><td style="padding:11px 16px;text-align:center;color:#555">0.831</td><td style="padding:11px 16px;text-align:center;color:#2dc653">✓</td></tr> |
| <tr style="border-bottom:1px solid #111"><td style="padding:11px 16px;color:#555">Stanford</td><td style="padding:11px 16px;color:#2a2a2a;font-size:0.8rem">0051161</td><td style="padding:11px 16px;text-align:center"><span style="color:#2dc653;font-weight:700">TC</span></td><td style="padding:11px 16px;text-align:center"><span style="color:#2dc653;font-weight:700">TC</span></td><td style="padding:11px 16px;text-align:center;color:#555">0.127</td><td style="padding:11px 16px;text-align:center;color:#2dc653">✓</td></tr> |
| <tr style="border-bottom:1px solid #111"><td style="padding:11px 16px;color:#555">Trinity</td><td style="padding:11px 16px;color:#2a2a2a;font-size:0.8rem">0050232</td><td style="padding:11px 16px;text-align:center"><span style="color:#e63946;font-weight:700">ASD</span></td><td style="padding:11px 16px;text-align:center"><span style="color:#f4a261;font-weight:700">INCONCL.</span></td><td style="padding:11px 16px;text-align:center;color:#555">0.487</td><td style="padding:11px 16px;text-align:center;color:#f4a261;font-size:0.8rem">⚠ review</td></tr> |
| <tr style="border-bottom:1px solid #111"><td style="padding:11px 16px;color:#555">Trinity</td><td style="padding:11px 16px;color:#2a2a2a;font-size:0.8rem">0050233</td><td style="padding:11px 16px;text-align:center"><span style="color:#2dc653;font-weight:700">TC</span></td><td style="padding:11px 16px;text-align:center"><span style="color:#2dc653;font-weight:700">TC</span></td><td style="padding:11px 16px;text-align:center;color:#555">0.241</td><td style="padding:11px 16px;text-align:center;color:#2dc653">✓</td></tr> |
| <tr style="border-bottom:1px solid #111"><td style="padding:11px 16px;color:#555">Yale</td><td style="padding:11px 16px;color:#2a2a2a;font-size:0.8rem">0050551</td><td style="padding:11px 16px;text-align:center"><span style="color:#e63946;font-weight:700">ASD</span></td><td style="padding:11px 16px;text-align:center"><span style="color:#e63946;font-weight:700">ASD</span></td><td style="padding:11px 16px;text-align:center;color:#555">0.689</td><td style="padding:11px 16px;text-align:center;color:#2dc653">✓</td></tr> |
| <tr><td style="padding:11px 16px;color:#555">Yale</td><td style="padding:11px 16px;color:#2a2a2a;font-size:0.8rem">0050552</td><td style="padding:11px 16px;text-align:center"><span style="color:#2dc653;font-weight:700">TC</span></td><td style="padding:11px 16px;text-align:center"><span style="color:#2dc653;font-weight:700">TC</span></td><td style="padding:11px 16px;text-align:center;color:#555">0.156</td><td style="padding:11px 16px;text-align:center;color:#2dc653">✓</td></tr> |
| </tbody> |
| </table> |
| </div> |
| <div style="margin-top:14px;color:#222;font-size:0.78rem;line-height:1.7"> |
| Inconclusive predictions (0.4 < p < 0.6) surface borderline cases for clinical review rather than forcing a wrong label. |
| Zero confident misclassifications across all 5 unseen sites. |
| </div> |
| </div> |
| """ |
|
|
| ARCHITECTURE = """ |
| <div style="padding:8px 0"> |
| <div style="font-size:0.7rem;color:#e63946;letter-spacing:4px;text-transform:uppercase;margin-bottom:24px">Adversarial Brain-Mode GCN · Architecture</div> |
| |
| <div style="display:flex;gap:12px;margin-bottom:20px;flex-wrap:wrap"> |
| <div style="background:#0f0f0f;border:1px solid #1e1e1e;border-radius:12px;padding:20px 24px;flex:1;min-width:200px"> |
| <div style="color:#f4a261;font-weight:700;font-size:0.85rem;margin-bottom:10px;text-transform:uppercase;letter-spacing:1px">Brain Mode Decomposition</div> |
| <div style="color:#444;font-size:0.84rem;line-height:1.8"> |
| K=16 learnable directions in ROI space.<br> |
| <span style="color:#888;font-family:monospace;font-size:0.82rem">M_kl = v_k · FC · v_l</span><br> |
| Compresses 19,900 FC features → 152 dims while preserving network structure. Each mode specializes to DMN, salience, FPN. |
| </div> |
| </div> |
| <div style="background:#0f0f0f;border:1px solid #1e1e1e;border-radius:12px;padding:20px 24px;flex:1;min-width:200px"> |
| <div style="color:#f4a261;font-weight:700;font-size:0.85rem;margin-bottom:10px;text-transform:uppercase;letter-spacing:1px">Gradient Reversal Layer</div> |
| <div style="color:#444;font-size:0.84rem;line-height:1.8"> |
| Adversarial site deconfounding (Ganin et al. 2016). Encoder minimizes ASD loss while maximizing site confusion — forcing site-invariant representations. α annealed 0→1 across training. |
| </div> |
| </div> |
| <div style="background:#0f0f0f;border:1px solid #1e1e1e;border-radius:12px;padding:20px 24px;flex:1;min-width:200px"> |
| <div style="color:#f4a261;font-weight:700;font-size:0.85rem;margin-bottom:10px;text-transform:uppercase;letter-spacing:1px">LOSO Ensemble</div> |
| <div style="color:#444;font-size:0.84rem;line-height:1.8"> |
| 4 models × 1 held-out site each. At inference, average all 4 probabilities. No model ever saw the test subject's scanner site. Cross-model agreement = site-independent finding. |
| </div> |
| </div> |
| </div> |
| |
| <div style="background:#0f0f0f;border:1px solid #1e1e1e;border-radius:12px;padding:24px;margin-bottom:16px;font-family:monospace;font-size:0.82rem;line-height:2.1;color:#333"> |
| <span style="color:#e63946">fMRI BOLD</span> (T × 200 ROIs) <span style="color:#1e1e1e">←── CC200 atlas</span><br> |
| │<br> |
| ┌──┴───────────────────┐<br> |
| │ │<br> |
| <span style="color:#777">FC matrix</span> (200×200) <span style="color:#777">BOLD windows</span> (30×200)<br> |
| │ │<br> |
| └──────────┬───────────┘<br> |
| │<br> |
| <span style="color:#f4a261">Brain Mode Decomposition</span> K=16<br> |
| M_kl = v_k · FC · v_l + std(v_k · bold)<br> |
| │ 152 features<br> |
| <span style="color:#f4a261">Shared Encoder</span> (MLP, dim=64)<br> |
| │<br> |
| ┌──────┴──────────────────┐<br> |
| │ │<br> |
| <span style="color:#2dc653">ASD head</span> <span style="color:#1e1e1e">GRL(α) → site head</span><br> |
| minimize CE(ASD) <span style="color:#1a1a1a">maximize site confusion</span><br> |
| │<br> |
| <span style="color:#e63946">p(ASD)</span> + <span style="color:#777">gradient saliency on FC (real-time)</span> |
| </div> |
| |
| <div style="background:#0f0f0f;border:1px solid #1e1e1e;border-radius:12px;overflow:hidden"> |
| <table style="width:100%;border-collapse:collapse;font-size:0.85rem"> |
| <tr style="border-bottom:1px solid #111"><td style="padding:11px 16px;color:#333;width:180px">Dataset</td><td style="padding:11px 16px;color:#888">ABIDE I — 1,102 subjects, 17 acquisition sites</td></tr> |
| <tr style="border-bottom:1px solid #111"><td style="padding:11px 16px;color:#333">Parcellation</td><td style="padding:11px 16px;color:#888">CC200 (Craddock 2012) — 200 functional ROIs</td></tr> |
| <tr style="border-bottom:1px solid #111"><td style="padding:11px 16px;color:#333">Architecture</td><td style="padding:11px 16px;color:#888">AdversarialBrainModeNetwork — K=16 modes, hidden_dim=64</td></tr> |
| <tr style="border-bottom:1px solid #111"><td style="padding:11px 16px;color:#333">Regularization</td><td style="padding:11px 16px;color:#888">GRL adversarial + orthogonality loss on brain modes</td></tr> |
| <tr style="border-bottom:1px solid #111"><td style="padding:11px 16px;color:#333">Validation</td><td style="padding:11px 16px;color:#888">LOSO AUC = <b style="color:#e63946">0.7872</b> across 529 held-out subjects</td></tr> |
| <tr><td style="padding:11px 16px;color:#333">Interpretability</td><td style="padding:11px 16px;color:#888">Real-time gradient saliency on 200×200 FC adjacency matrix</td></tr> |
| </table> |
| </div> |
| </div> |
| """ |
|
|
| AMD = """ |
| <div style="padding:8px 0"> |
| <div style="font-size:0.7rem;color:#f4a261;letter-spacing:4px;text-transform:uppercase;margin-bottom:24px">AMD Instinct MI300X · Qwen2.5-7B Clinical Fine-Tune</div> |
| |
| <div style="display:flex;gap:12px;margin-bottom:24px;flex-wrap:wrap"> |
| <div style="background:#0f0f0f;border:1px solid #1e1e1e;border-radius:12px;padding:24px;flex:1;min-width:120px;text-align:center"> |
| <div style="font-size:2.4rem;font-weight:900;color:#f4a261;line-height:1">192</div> |
| <div style="color:#2a2a2a;font-size:0.7rem;margin-top:6px;text-transform:uppercase;letter-spacing:1px">GB HBM3</div> |
| </div> |
| <div style="background:#0f0f0f;border:1px solid #1e1e1e;border-radius:12px;padding:24px;flex:1;min-width:120px;text-align:center"> |
| <div style="font-size:2.4rem;font-weight:900;color:#f4a261;line-height:1">bf16</div> |
| <div style="color:#2a2a2a;font-size:0.7rem;margin-top:6px;text-transform:uppercase;letter-spacing:1px">No quantization</div> |
| </div> |
| <div style="background:#0f0f0f;border:1px solid #1e1e1e;border-radius:12px;padding:24px;flex:1;min-width:120px;text-align:center"> |
| <div style="font-size:2.4rem;font-weight:900;color:white;line-height:1">7B</div> |
| <div style="color:#2a2a2a;font-size:0.7rem;margin-top:6px;text-transform:uppercase;letter-spacing:1px">Qwen2.5 params</div> |
| </div> |
| <div style="background:#0f0f0f;border:1px solid #1e1e1e;border-radius:12px;padding:24px;flex:1;min-width:120px;text-align:center"> |
| <div style="font-size:2.4rem;font-weight:900;color:white;line-height:1">2K</div> |
| <div style="color:#2a2a2a;font-size:0.7rem;margin-top:6px;text-transform:uppercase;letter-spacing:1px">Domain examples</div> |
| </div> |
| <div style="background:#0f0f0f;border:1px solid #1e1e1e;border-radius:12px;padding:24px;flex:1;min-width:120px;text-align:center"> |
| <div style="font-size:2.4rem;font-weight:900;color:white;line-height:1">r=16</div> |
| <div style="color:#2a2a2a;font-size:0.7rem;margin-top:6px;text-transform:uppercase;letter-spacing:1px">LoRA rank</div> |
| </div> |
| </div> |
| |
| <div style="background:#0f0f0f;border:1px solid #1e1e1e;border-radius:12px;overflow:hidden;margin-bottom:16px"> |
| <table style="width:100%;border-collapse:collapse;font-size:0.85rem"> |
| <tr style="border-bottom:1px solid #111"><td style="padding:11px 16px;color:#333;width:180px">Base model</td><td style="padding:11px 16px;color:#888">Qwen/Qwen2.5-7B-Instruct <span style="color:#2a2a2a">(AMD partner model)</span></td></tr> |
| <tr style="border-bottom:1px solid #111"><td style="padding:11px 16px;color:#333">Method</td><td style="padding:11px 16px;color:#888">LoRA r=16, α=32 — all projection layers (q, k, v, o, gate, up, down)</td></tr> |
| <tr style="border-bottom:1px solid #111"><td style="padding:11px 16px;color:#333">Hardware</td><td style="padding:11px 16px;color:#888">AMD Instinct MI300X · ROCm · bf16 — full precision, no quantization needed</td></tr> |
| <tr style="border-bottom:1px solid #111"><td style="padding:11px 16px;color:#333">Training data</td><td style="padding:11px 16px;color:#888">2,000 GCN→clinical report pairs · ASD neuroscience grounded · 3 epochs</td></tr> |
| <tr style="border-bottom:1px solid #111"><td style="padding:11px 16px;color:#333">Task</td><td style="padding:11px 16px;color:#888">Structured clinical interpretation of LOSO GCN ensemble outputs</td></tr> |
| <tr><td style="padding:11px 16px;color:#333">Output</td><td style="padding:11px 16px;color:#888">DMN / salience / cerebellar-cortical findings grounded in ASD literature</td></tr> |
| </table> |
| </div> |
| |
| <div style="display:flex;gap:12px;flex-wrap:wrap"> |
| <div style="background:#0f0f0f;border:1px solid #1e1e1e;border-radius:12px;padding:20px 24px;flex:1;min-width:240px"> |
| <div style="color:#f4a261;font-weight:700;font-size:0.85rem;margin-bottom:10px;text-transform:uppercase;letter-spacing:1px">Why Qwen2.5-7B?</div> |
| <div style="color:#444;font-size:0.84rem;line-height:1.7">Qwen is an AMD partner model. Fine-tuning on MI300X with an AMD-aligned model demonstrates the complete AMD AI stack. The 192 GB HBM3 unified memory enables full bf16 fine-tuning impossible on consumer hardware.</div> |
| </div> |
| <div style="background:#0f0f0f;border:1px solid #1e1e1e;border-radius:12px;padding:20px 24px;flex:1;min-width:240px"> |
| <div style="color:#f4a261;font-weight:700;font-size:0.85rem;margin-bottom:10px;text-transform:uppercase;letter-spacing:1px">Why domain fine-tuning?</div> |
| <div style="color:#444;font-size:0.84rem;line-height:1.7">Base Qwen generates generic text. Fine-tuned Qwen understands what "3/4 site-blind models agree" means clinically, grounds reports in ASD neuroscience (DMN, salience network, cerebellar-cortical coupling), and calibrates to our specific GCN output format.</div> |
| </div> |
| </div> |
| </div> |
| """ |
|
|
| |
|
|
| css = """ |
| body { background: #0d0d0d; } |
| .gradio-container { max-width: 1100px !important; margin: auto; padding: 0 24px; } |
| .tab-nav { border-bottom: 1px solid #111 !important; margin-bottom: 8px; } |
| .tab-nav button { color: #333 !important; font-size: 0.85rem !important; padding: 12px 20px !important; letter-spacing: 0.5px; } |
| .tab-nav button.selected { color: #fff !important; border-bottom: 2px solid #e63946 !important; } |
| footer { display: none !important; } |
| """ |
|
|
| with gr.Blocks(title="BrainConnect-ASD", css=css, theme=gr.themes.Base()) as demo: |
| gr.HTML(HEADER) |
|
|
| with gr.Tabs(): |
| with gr.Tab("🔬 Analysis"): |
| file_input = gr.File(label="Upload CC200 fMRI file (.1D or .npz)", type="filepath") |
| verdict_html = gr.HTML() |
| ens_html = gr.HTML() |
| gr.HTML("<div style='margin-top:20px;font-size:0.7rem;color:#222;letter-spacing:3px;text-transform:uppercase;margin-bottom:8px'>Gradient Saliency · which brain connections drove this prediction</div>") |
| sal_img = gr.Image(label="", type="pil", show_label=False) |
| rep_html = gr.HTML() |
| file_input.change(fn=run_gcn, inputs=file_input, |
| outputs=[verdict_html, ens_html, rep_html, sal_img]) |
|
|
| with gr.Tab("📊 Validation"): |
| gr.HTML(VALIDATION) |
|
|
| with gr.Tab("🧠 Architecture"): |
| gr.HTML(ARCHITECTURE) |
|
|
| with gr.Tab("⚡ AMD MI300X"): |
| gr.HTML(AMD) |
|
|
| gr.HTML(""" |
| <div style="text-align:center;padding:32px 0 16px;color:#1a1a1a;font-size:0.75rem;border-top:1px solid #0f0f0f;margin-top:12px"> |
| Adversarial Brain-Mode GCN (k=16) · ABIDE I 1,102 subjects · |
| Qwen2.5-7B LoRA on AMD Instinct MI300X · |
| <a href="https://github.com/Yatsuiii/Brain-Connectivity-GCN" style="color:#222">GitHub</a> |
| </div>""") |
|
|
| print("Preloading models...") |
| get_models() |
| print("Ready.") |
|
|
| if __name__ == "__main__": |
| demo.launch() |
|
|
