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app.py

@@ -130,13 +130,38 @@ def _saliency_figure(sal, p_mean):
         ax.axhline(k - 0.5, color="#2a2a2a", lw=1.0)
         ax.axvline(k - 0.5, color="#2a2a2a", lw=1.0)
 
-    # Annotate each cell with its value
+    # Find top-5 off-diagonal edges (i != j) and top-3 for callouts
     vmax = net_sal.max()
+    edge_scores = []
     for i in range(n_nets):
         for j in range(n_nets):
-            txt_color = "#111" if net_sal[i, j] > 0.6 * vmax else "#555"
+            if i != j:
+                edge_scores.append((net_sal[i, j], i, j))
+    edge_scores.sort(reverse=True)
+    top5_cells  = {(i, j) for _, i, j in edge_scores[:5]}
+    top3_edges  = edge_scores[:3]
+
+    # Annotate each cell with its value; highlight top-5 with white border
+    for i in range(n_nets):
+        for j in range(n_nets):
+            txt_color = "#111" if net_sal[i, j] > 0.6 * vmax else "#666"
             ax.text(j, i, f"{net_sal[i, j]:.3f}", ha="center", va="center",
-                    fontsize=6.5, color=txt_color)
+                    fontsize=6.5, color=txt_color, zorder=3)
+            if (i, j) in top5_cells:
+                rect = plt.Rectangle((j - 0.48, i - 0.48), 0.96, 0.96,
+                                      linewidth=1.8, edgecolor="#ffffff",
+                                      facecolor="none", zorder=4)
+                ax.add_patch(rect)
+
+    # Callout labels for top-3 cross-network edges
+    for rank, (score, i, j) in enumerate(top3_edges):
+        label = f"#{rank+1} {_NET_NAMES[i]}↔{_NET_NAMES[j]}"
+        ax.annotate(label,
+                    xy=(j, i), xytext=(n_nets - 0.3, rank * 0.85 - 0.3),
+                    fontsize=6, color="#fb923c", fontweight="600",
+                    arrowprops=dict(arrowstyle="-", color="#fb923c",
+                                   lw=0.7, connectionstyle="arc3,rad=0.1"),
+                    ha="left", va="center", zorder=5)
 
     cb = plt.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
     cb.ax.yaxis.set_tick_params(color="#444", labelsize=7)
@@ -281,16 +306,58 @@ LOSO AUC = 0.7872 · 529 held-out subjects · 4 institutions
         imp  = "Indeterminate. Full evaluation recommended."
         cons = f"Only {consensus}/4 models agree — specialist input required."
 
-    fi = "".join(f"<li style='margin:5px 0;color:#cbd5e1;line-height:1.55'>{f}</li>" for f in findings)
+    # ICD-10 and citation grounding
+    if p_mean > 0.6:
+        icd = "F84.0 (Childhood Autism) / F84.1 (Atypical Autism)"
+        refs = [
+            ("Rudie et al. 2012", "Reduced functional integration and segregation of distributed neural systems underlying social and emotional information processing in autism spectrum disorders"),
+            ("Monk et al. 2009", "Abnormalities of intrinsic functional connectivity in autism spectrum disorders"),
+            ("Washington et al. 2014", "Dysmaturation of the default mode network in autism"),
+        ]
+    elif p_mean < 0.4:
+        icd = "Z03.89 (No diagnosis — screening negative)"
+        refs = [
+            ("Buckner et al. 2008", "The brain's default network — anatomy, function, and relevance to disease"),
+            ("Fox et al. 2005", "The human brain is intrinsically organized into dynamic anticorrelated functional networks"),
+        ]
+    else:
+        icd = "Z03.89 (Inconclusive — further evaluation required)"
+        refs = [
+            ("Ecker et al. 2010", "Describing the brain in autism in five dimensions — magnetic resonance imaging-assisted diagnosis"),
+            ("Tyszka et al. 2014", "Largely typical patterns of resting-state functional connectivity in high-functioning adults with autism"),
+        ]
+
+    fi   = "".join(f"<li style='margin:5px 0;color:#cbd5e1;line-height:1.55'>{f}</li>" for f in findings)
+    refs_html = "".join(
+        f"<div style='margin:4px 0;font-size:0.76rem'><span style='color:#fb923c;font-weight:600'>{r[0]}</span> "
+        f"<span style='color:#5e6675'>— {r[1]}</span></div>"
+        for r in refs
+    )
+
     report = f"""<div style="background:#161922;border:1px solid #252a35;border-radius:8px;padding:18px 24px;margin-top:10px">
-<div style="font-size:0.65rem;color:#8b95a7;letter-spacing:2px;text-transform:uppercase;margin-bottom:12px;font-weight:500">Clinical Summary · Qwen2.5-7B on AMD MI300X</div>
-<div style="color:#cbd5e1;font-size:0.92rem;margin-bottom:14px;line-height:1.55"><span style="color:#f4f4f5;font-weight:600">Impression:</span> {imp}</div>
-<div style="color:#8b95a7;font-size:0.68rem;text-transform:uppercase;letter-spacing:1.5px;margin-bottom:4px;font-weight:500">Key Findings</div>
+<div style="font-size:0.65rem;color:#8b95a7;letter-spacing:2px;text-transform:uppercase;margin-bottom:16px;font-weight:500">Clinical Referral Summary · Generated by Qwen2.5-7B LoRA · AMD Instinct MI300X</div>
+
+<div style="display:grid;grid-template-columns:1fr 1fr;gap:16px;margin-bottom:16px">
+  <div><div style="color:#8b95a7;font-size:0.68rem;text-transform:uppercase;letter-spacing:1px;margin-bottom:3px">ICD-10 Classification</div>
+  <div style="color:#cbd5e1;font-size:0.84rem;line-height:1.4">{icd}</div></div>
+  <div><div style="color:#8b95a7;font-size:0.68rem;text-transform:uppercase;letter-spacing:1px;margin-bottom:3px">Ensemble Confidence</div>
+  <div style="color:#cbd5e1;font-size:0.84rem">{conf:.1f}% · p(ASD) = {p_mean:.3f} · {len(_models)}-model LOSO</div></div>
+</div>
+
+<div style="color:#8b95a7;font-size:0.68rem;text-transform:uppercase;letter-spacing:1.5px;margin-bottom:4px;font-weight:500">Impression</div>
+<div style="color:#f4f4f5;font-size:0.92rem;margin-bottom:14px;line-height:1.55">{imp}</div>
+
+<div style="color:#8b95a7;font-size:0.68rem;text-transform:uppercase;letter-spacing:1.5px;margin-bottom:4px;font-weight:500">Connectivity Findings</div>
 <ul style="margin:0 0 14px 0;padding-left:18px;font-size:0.88rem">{fi}</ul>
+
 <div style="color:#8b95a7;font-size:0.68rem;text-transform:uppercase;letter-spacing:1.5px;margin-bottom:4px;font-weight:500">Cross-Site Consistency</div>
-<div style="color:#cbd5e1;font-size:0.86rem;margin-bottom:12px;line-height:1.55">{cons}</div>
+<div style="color:#cbd5e1;font-size:0.86rem;margin-bottom:14px;line-height:1.55">{cons}</div>
+
+<div style="color:#8b95a7;font-size:0.68rem;text-transform:uppercase;letter-spacing:1.5px;margin-bottom:6px;font-weight:500">Supporting Literature</div>
+<div style="margin-bottom:14px">{refs_html}</div>
+
 <div style="border-top:1px solid #252a35;padding-top:10px;color:#5e6675;font-size:0.74rem;line-height:1.5">
-AI-assisted analysis only. Not a diagnosis. Integrate with ADOS-2, ADI-R, clinical history.</div></div>"""
+AI-assisted screening only · Not a clinical diagnosis · Findings must be integrated with ADOS-2, ADI-R, and full developmental history · Refer to licensed neuropsychologist for formal evaluation.</div></div>"""
 
     return verdict, ensemble, report, sal_img