| |
| |
| """ |
| Build a single self-contained HTML report of the benchmark. |
| 4 modules (Myopia / AMD / Glaucoma / DR). Each dataset card shows: |
| - 采集背景 (acquisition background: FOV / device / source / resolution) |
| - 类别分布 (class distribution by split: table + grouped bar chart) |
| - 模型性能 (metrics table + grouped bar chart, 3 models) |
| - 可展开的混淆矩阵 / ROC 图 |
| All images are embedded as base64 -> works offline, one file. |
| """ |
| import os, json, base64, io, csv |
| from collections import defaultdict |
| import numpy as np |
| import matplotlib |
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
|
|
| PROJ = "/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image" |
| RESULTS = f"{PROJ}/results" |
| DSROOT = f"{PROJ}/Dataset" |
| OUT = f"{RESULTS}/report.html" |
|
|
| MODELS = ["retfound", "resnet", "vit"] |
| MLABEL = {"retfound": "RetFound (ViT-L, CFP)", "resnet": "ResNet-50", "vit": "ViT-B/16"} |
| MCOLOR = {"retfound": "#4C72B0", "resnet": "#55A868", "vit": "#C44E52"} |
| SPLIT_COLOR = {"train": "#4C72B0", "val": "#DD8452", "test": "#55A868"} |
|
|
| CATS = [ |
| ("近视 · Myopic Maculopathy", "#2563eb", [("mmac", "MMAC 2023", "5-class grade 0–4")]), |
| ("AMD · Age-related Macular Degeneration", "#16a34a", [("adam", "ADAM", "binary AMD / Non-AMD")]), |
| ("青光眼 · Glaucoma", "#d97706", [("airogs", "AIROGS (EyePACS-AIROGS-light)", "binary RG / NRG"), |
| ("papila", "PAPILA", "binary glaucoma / healthy")]), |
| ("DR · Diabetic Retinopathy", "#dc2626", [("idrid", "IDRiD", "5-class grade 0–4"), |
| ("aptos", "APTOS-2019", "5-class grade 0–4"), |
| ("deepdrid", "DeepDRiD", "5-class grade 0–4")]), |
| ] |
| SPLITS = {"mmac": "973/139/279", "adam": "280/40/80", "airogs": "5000/540/1000", |
| "papila": "294/42/84", "idrid": "318/45/92", "aptos": "2930/366/366", |
| "deepdrid": "1200/400/400"} |
| DSPATH = {"mmac": "Myopia/Classification_of_Myopic_Maculopathy", "adam": "AMD/adamdataset", |
| "airogs": "Glaucoma/eyepacs-airogs-light", "papila": "Glaucoma/papila-retinal-fundus-images", |
| "idrid": "DR/idrid-dataset", "aptos": "DR/aptos2019", "deepdrid": "DR/deepdrid"} |
|
|
| |
| BG = { |
| "mmac": "彩色眼底照（非散瞳）｜FOV：45°（设备标称，论文正文未印）｜设备：Topcon TRC-NW400（单一设备）｜来源：上海健康医学中心 + 上海市第六人民医院（中国，均为中国人群）｜分辨率：未公开｜标注：META-PM 5 级，双医师分级（κ=0.91），单设备单人群为其局限。", |
| "adam": "彩色眼底照｜FOV：未标注（仅说明取景中心为视盘 / 黄斑 / 两者中点）｜设备：Zeiss Visucam 500（2124×2056，824 张）+ Canon CR-2（1444×1444，376 张）｜来源：中山眼科中心（中国·广州）｜Training400：89 AMD / 311 非 AMD（AMD 被刻意过采样，非真实患病率）。", |
| "airogs": "彩色眼底照，源自 EyePACS 远程筛查平台（美国约 500 个点、60071 人、多种族）｜设备：多相机混用（Optovue iCam100≈26%、Topcon NW200/400≈20%、Canon CR1/CR2/DGI、Centervue、Nidek、Crystalvue，约 21% 未知）｜FOV / 分辨率：因多设备未统一｜原为糖网筛查图后重标青光眼；全集 RG 仅约 3%（极不平衡），本「light」子集已平衡为 3270/3270。", |
| "papila": "彩色眼底照，以视盘为中心｜FOV：30°｜设备：Topcon TRC-NW400（非散瞳）｜分辨率：2576×1934 JPEG｜来源：Reina Sofía 大学医院（西班牙·Murcia，2018–2020）｜244 人双眼共 488 张（healthy/glaucoma/suspect，本项目已剔除 suspect → 420）｜附临床数据与视盘/视杯分割。", |
| "idrid": "彩色眼底照｜FOV：50°｜设备：Kowa VX-10α（散瞳，托吡卡胺 0.5%）｜分辨率：4288×2848 JPG｜来源：印度 Nanded（Maharashtra）眼科诊所，2009–2017｜全集 516 张（本项目有标签 455 张）｜DR 0–4（ICDR）+ 黄斑水肿风险分级。", |
| "aptos": "彩色眼底照｜设备 / FOV / 分辨率：均未公开（多诊所、多相机、跨时间采集，异质性大）｜来源：Aravind 眼科医院（印度），乡村远程筛查｜训练集 3662 张，DR 0–4（ICDR）｜真实世界噪声明显（伪影 / 失焦 / 过曝欠曝 / 标签噪声）。", |
| "deepdrid": "彩色眼底照（常规，非超广角）｜设备：Topcon 非散瞳（具体型号未公开）｜FOV≈45–60°、分辨率≈1956×1934（来自补充材料，中等可信）｜来源：上海市第六人民医院（中国）糖尿病筛查队列｜2000 张 / 500 人，每眼双视野（视盘中心 + 黄斑中心）｜DR 0–4 + 图像质量标注。", |
| } |
|
|
| BIN_COLS = [("accuracy", "Accuracy"), ("auroc", "AUROC"), ("auprc", "AUPRC"), ("f1_macro", "F1"), |
| ("sensitivity", "Sensitivity"), ("specificity", "Specificity"), ("cohen_kappa", "Kappa"), ("mcc", "MCC")] |
| MUL_COLS = [("accuracy", "Accuracy"), ("balanced_accuracy", "Bal-Acc"), ("auroc_macro_ovr", "macro-AUROC"), |
| ("quadratic_weighted_kappa", "QWK"), ("f1_macro", "F1-macro"), ("precision_macro", "Prec-macro"), |
| ("recall_macro", "Rec-macro"), ("cohen_kappa", "Kappa")] |
| BIN_BAR = [("accuracy", "Acc"), ("auroc", "AUROC"), ("auprc", "AUPRC"), ("f1_macro", "F1"), |
| ("sensitivity", "Sens"), ("specificity", "Spec")] |
| MUL_BAR = [("accuracy", "Acc"), ("auroc_macro_ovr", "AUROC"), ("quadratic_weighted_kappa", "QWK"), |
| ("f1_macro", "F1"), ("balanced_accuracy", "Bal-Acc")] |
|
|
|
|
| def load(dsk, model): |
| try: |
| return json.load(open(os.path.join(RESULTS, dsk, model, "metrics.json"))) |
| except Exception: |
| return None |
|
|
|
|
| def read_dist(dsk): |
| """Return ordered [(label,class_name)], counts[split][label], total.""" |
| rows = list(csv.DictReader(open(os.path.join(DSROOT, DSPATH[dsk], "labels.csv")))) |
| classes = sorted(set((r["label"], r.get("class_name", "")) for r in rows), key=lambda x: int(x[0])) |
| cnt = defaultdict(lambda: defaultdict(int)) |
| for r in rows: |
| cnt[r["split"]][r["label"]] += 1 |
| return classes, cnt, len(rows) |
|
|
|
|
| def b64_img(path): |
| try: |
| return "data:image/png;base64," + base64.b64encode(open(path, "rb").read()).decode() |
| except Exception: |
| return "" |
|
|
|
|
| def fig_b64(fig): |
| buf = io.BytesIO() |
| fig.savefig(buf, format="png", dpi=110, bbox_inches="tight") |
| plt.close(fig) |
| return "data:image/png;base64," + base64.b64encode(buf.getvalue()).decode() |
|
|
|
|
| def grouped_bar(labels, series, colors, ylabel, intlabels=False): |
| """series: list of (name, [values]) aligned to labels.""" |
| x = np.arange(len(labels)) |
| w = 0.8 / max(1, len(series)) |
| fig, ax = plt.subplots(figsize=(7.2, 3.6)) |
| for i, (name, vals) in enumerate(series): |
| bars = ax.bar(x + (i - (len(series) - 1) / 2) * w, vals, w, label=name, color=colors[name]) |
| for b, v in zip(bars, vals): |
| if v: |
| ax.text(b.get_x() + b.get_width() / 2, v, (f"{int(v)}" if intlabels else f"{v:.2f}"), |
| ha="center", va="bottom", fontsize=6.3) |
| ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=8.5) |
| ax.set_ylabel(ylabel) |
| ax.legend(fontsize=7.5, ncol=len(series), loc="lower center", bbox_to_anchor=(0.5, 1.0), frameon=False) |
| ax.grid(axis="y", ls=":", alpha=0.4); ax.set_axisbelow(True) |
| if not intlabels: |
| ax.set_ylim(0, 1.08) |
| for s in ("top", "right"): |
| ax.spines[s].set_visible(False) |
| return fig_b64(fig) |
|
|
|
|
| def perf_bar(metrics, bar_keys): |
| keys = [k for k, _ in bar_keys] |
| series = [(MLABEL[m], [(metrics.get(m) or {}).get(k) if isinstance((metrics.get(m) or {}).get(k), (int, float)) else 0 |
| for k in keys]) for m in MODELS] |
| colors = {MLABEL[m]: MCOLOR[m] for m in MODELS} |
| return grouped_bar([l for _, l in bar_keys], series, colors, "score", intlabels=False) |
|
|
|
|
| def dist_bar(classes, cnt): |
| labels = [f"{l}·{cn}" for l, cn in classes] |
| series = [(sp, [cnt[sp].get(l, 0) for l, _ in classes]) for sp in ("train", "val", "test")] |
| return grouped_bar(labels, series, SPLIT_COLOR, "images", intlabels=True) |
|
|
|
|
| def perf_table(metrics, cols): |
| keys = [k for k, _ in cols] |
| best = {} |
| for k in keys: |
| vals = [(metrics[m].get(k) if metrics.get(m) and isinstance(metrics[m].get(k), (int, float)) else None) for m in MODELS] |
| vals = [v for v in vals if v is not None] |
| best[k] = max(vals) if vals else None |
| h = ['<table class="metrics"><thead><tr><th>Model</th>'] + [f"<th>{lab}</th>" for _, lab in cols] + ["</tr></thead><tbody>"] |
| for m in MODELS: |
| mm = metrics.get(m) or {} |
| h.append(f'<tr><td class="model"><span class="dot" style="background:{MCOLOR[m]}"></span>{MLABEL[m]}</td>') |
| for k, _ in cols: |
| v = mm.get(k) |
| if isinstance(v, (int, float)): |
| cls = "best" if (best[k] is not None and abs(v - best[k]) < 1e-9) else "" |
| h.append(f'<td class="{cls}">{v:.4f}</td>') |
| else: |
| h.append("<td>—</td>") |
| h.append("</tr>") |
| h.append("</tbody></table>") |
| return "".join(h) |
|
|
|
|
| def dist_table(classes, cnt): |
| h = ['<table class="metrics"><thead><tr><th>Split</th>'] + [f"<th>{l}·{cn}</th>" for l, cn in classes] + ["<th>合计</th></tr></thead><tbody>"] |
| coltot = {l: 0 for l, _ in classes} |
| for sp in ("train", "val", "test"): |
| h.append(f'<tr><td class="model">{sp}</td>') |
| s = 0 |
| for l, _ in classes: |
| v = cnt[sp].get(l, 0); s += v; coltot[l] += v |
| h.append(f"<td>{v}</td>") |
| h.append(f"<td><b>{s}</b></td></tr>") |
| h.append('<tr><td class="model">合计</td>') |
| for l, _ in classes: |
| h.append(f"<td><b>{coltot[l]}</b></td>") |
| h.append(f'<td><b>{sum(coltot.values())}</b></td></tr></tbody></table>') |
| return "".join(h) |
|
|
|
|
| def perclass_table(metrics, classes): |
| """Per-class Recall / F1 / AUROC for each model + shared support. Best F1 per class highlighted.""" |
| def fmt(v): |
| return f"{v:.3f}" if isinstance(v, (int, float)) else "—" |
| h = ['<table class="metrics pc"><thead><tr><th rowspan="2">Class</th><th rowspan="2">Support</th>'] |
| for m in MODELS: |
| h.append(f'<th colspan="3" class="grp">{MLABEL[m]}</th>') |
| h.append("</tr><tr>") |
| for m in MODELS: |
| h.append('<th class="grp">Recall</th><th>F1</th><th>AUROC</th>') |
| h.append("</tr></thead><tbody>") |
| for l, cn in classes: |
| sup = None |
| for m in MODELS: |
| pc = (metrics.get(m) or {}).get("per_class", {}) |
| if str(l) in pc: |
| sup = int(pc[str(l)].get("support", 0)); break |
| f1s = [(metrics.get(m) or {}).get("per_class", {}).get(str(l), {}).get("f1-score") for m in MODELS] |
| bf = max([v for v in f1s if isinstance(v, (int, float))], default=None) |
| h.append(f'<tr><td class="model">{l}·{cn}</td><td>{sup if sup is not None else "—"}</td>') |
| for m in MODELS: |
| mm = metrics.get(m) or {} |
| pc = mm.get("per_class", {}).get(str(l), {}) |
| au = (mm.get("auroc_per_class") or {}).get(str(l)) |
| f1 = pc.get("f1-score") |
| f1cls = "best" if (isinstance(f1, (int, float)) and bf is not None and abs(f1 - bf) < 1e-9) else "" |
| h.append(f'<td class="grp">{fmt(pc.get("recall"))}</td><td class="{f1cls}">{fmt(f1)}</td><td>{fmt(au)}</td>') |
| h.append("</tr>") |
| h.append("</tbody></table>") |
| return "".join(h) |
|
|
|
|
| def perclass_f1_bar(metrics, classes): |
| labels = [f"{l}·{cn}" for l, cn in classes] |
| series = [] |
| for m in MODELS: |
| pc = (metrics.get(m) or {}).get("per_class", {}) |
| vals = [pc.get(str(l), {}).get("f1-score") if isinstance(pc.get(str(l), {}).get("f1-score"), (int, float)) else 0 |
| for l, _ in classes] |
| series.append((MLABEL[m], vals)) |
| return grouped_bar(labels, series, {MLABEL[m]: MCOLOR[m] for m in MODELS}, "per-class F1", intlabels=False) |
|
|
|
|
| def gallery_html(dsk): |
| rows = [] |
| for kind, title in [("confusion_matrix", "混淆矩阵"), ("roc", "ROC")]: |
| cells = [] |
| for m in MODELS: |
| img = b64_img(os.path.join(RESULTS, dsk, m, f"{kind}.png")) |
| if img: |
| cells.append(f'<figure><img src="{img}"><figcaption>{MLABEL[m]}</figcaption></figure>') |
| rows.append(f'<div class="grow"><h4>{title}</h4><div class="imgrow">{"".join(cells)}</div></div>') |
| return f'<details><summary>详细图：混淆矩阵 / ROC 曲线</summary><div class="gallery">{"".join(rows)}</div></details>' |
|
|
|
|
| def downsample_block(dsk): |
| """Per-dataset data-scarcity: for each fraction, a card with table + bar chart of all 4 metrics.""" |
| if dsk not in ("adam", "airogs", "papila"): |
| return "" |
| DS = os.path.join(RESULTS, "downsample") |
| frac_show = [100, 50, 25, 10, 5] |
| dnames = {"adam": "ADAM", "airogs": "AIROGS", "papila": "PAPILA"} |
| train_counts = {"adam": {100:280,50:140,25:70,10:28,5:14}, |
| "airogs": {100:5000,50:2500,25:1250,10:500,5:250}, |
| "papila": {100:294,50:146,25:73,10:29,5:15}} |
| metrics_keys = [("accuracy", "Acc"), ("auroc", "AUROC"), ("auprc", "AUPRC"), ("f1_macro", "F1"), ("sensitivity", "Sens"), ("specificity", "Spec")] |
| |
| frac_data = {} |
| for f in frac_show: |
| frac_data[f] = {} |
| for m in MODELS: |
| p = os.path.join(DS, dsk, f"{f:03d}", m, "metrics.json") |
| if os.path.isfile(p): |
| frac_data[f][m] = json.load(open(p)) |
| cards = [] |
| for f in frac_show: |
| labels = [kn for _, kn in metrics_keys] |
| series = [] |
| for m in MODELS: |
| mm = frac_data[f].get(m) |
| vals = [] |
| for k, _ in metrics_keys: |
| v = mm.get(k) if mm else None |
| vals.append(v if isinstance(v, (int, float)) else 0) |
| series.append((MLABEL[m], vals)) |
| bar = grouped_bar(labels, series, {MLABEL[m]: MCOLOR[m] for m in MODELS}, "score", intlabels=False) |
| |
| th = ['<table class="metrics"><thead><tr><th>Model</th>'] |
| for _, kn in metrics_keys: |
| th.append(f'<th>{kn}</th>') |
| th.append("</tr></thead><tbody>") |
| best_map = {} |
| for k, _ in metrics_keys: |
| bv = None |
| for m in MODELS: |
| mm = frac_data[f].get(m) |
| if mm: |
| v = mm.get(k) |
| if isinstance(v, (int, float)): |
| if bv is None or v > bv: |
| bv = v |
| best_map[k] = bv |
| for m in MODELS: |
| mm = frac_data[f].get(m) |
| th.append(f'<tr><td class="model"><span class="dot" style="background:{MCOLOR[m]}"></span>{m}</td>') |
| for k, _ in metrics_keys: |
| v = mm.get(k) if mm else None |
| cls = "best" if (isinstance(v, (int, float)) and best_map[k] is not None and abs(v - best_map[k]) < 1e-9) else "" |
| vs = f"{v:.4f}" if isinstance(v, (int, float)) else "—" |
| th.append(f'<td class="{cls}">{vs}</td>') |
| th.append("</tr>") |
| th.append("</tbody></table>") |
| cards.append(f""" |
| <div style="border:1px solid #eef0f3;border-radius:8px;margin:12px 20px;overflow:hidden"> |
| <div class="card-h" style="background:#fafbfc"><h4 style="font-size:14px;margin:0">{f}% · {train_counts[dsk][f]} 训练样本</h4><span class="meta">val/test 保持完整</span></div> |
| <div class="content" style="padding:10px 20px"> |
| <div class="tbl">{"".join(th)}</div> |
| <div class="chart"><img src="{bar}"></div> |
| </div> |
| </div>""") |
| return f""" |
| <h4 class="sec">数据稀缺性分析 · Data-scarcity experiment</h4> |
| <div class="bg" style="font-size:12px;color:#6b21a8">训练数据按类别分层抽样至 100/50/25/10/5%，保持 val/test 完整。<b>PAPILA 随数据量下降最快</b>，最适合作合成数据增广实验。</div> |
| {''.join(cards)}""" |
|
|
|
|
| def main(): |
| parts = [] |
| for cat_name, color, dsets in CATS: |
| cards = [] |
| for dsk, title, desc in dsets: |
| metrics = {m: load(dsk, m) for m in MODELS} |
| if not any(metrics.values()): |
| continue |
| task = next(v for v in metrics.values() if v)["task"] |
| cols = BIN_COLS if task == "binary" else MUL_COLS |
| bar = BIN_BAR if task == "binary" else MUL_BAR |
| ntest = next(v for v in metrics.values() if v).get("n_test") |
| classes, cnt, total = read_dist(dsk) |
| perclass_block = "" if task == "binary" else f""" |
| <h4 class="sec">每类指标 · Per-class metrics</h4> |
| <div class="content"> |
| <div class="tbl">{perclass_table(metrics, classes)}</div> |
| <div class="chart"><img src="{perclass_f1_bar(metrics, classes)}"></div> |
| </div>""" |
| cards.append(f""" |
| <div class="card"> |
| <div class="card-h"><h3>{title}</h3> |
| <span class="meta">{desc} · 划分 train/val/test = {SPLITS.get(dsk,'?')} · 总计 {total} 张</span></div> |
| <div class="bg"><b>📷 采集背景：</b>{BG.get(dsk,'')}</div> |
| <h4 class="sec">类别分布 · Class distribution（按 split）</h4> |
| <div class="content"> |
| <div class="tbl">{dist_table(classes, cnt)}</div> |
| <div class="chart"><img src="{dist_bar(classes, cnt)}"></div> |
| </div> |
| <h4 class="sec">模型性能 · Performance</h4> |
| <div class="content"> |
| <div class="tbl">{perf_table(metrics, cols)}</div> |
| <div class="chart"><img src="{perf_bar(metrics, bar)}"></div> |
| </div>{perclass_block} |
| {downsample_block(dsk)} |
| {gallery_html(dsk)} |
| </div>""") |
| parts.append(f'\n <section class="module" style="--accent:{color}">\n <h2>{cat_name}</h2>\n {"".join(cards)}\n </section>') |
|
|
| html = f"""<!doctype html><html lang="zh"><head><meta charset="utf-8"> |
| <meta name="viewport" content="width=device-width, initial-scale=1"> |
| <title>眼底图像分类 Benchmark</title> |
| <style> |
| :root{{font-family:-apple-system,"Segoe UI",Roboto,"PingFang SC","Microsoft YaHei",sans-serif}} |
| body{{margin:0;background:#f5f6f8;color:#1f2430;line-height:1.5}} |
| header{{background:linear-gradient(135deg,#1e293b,#334155);color:#fff;padding:32px 40px}} |
| header h1{{margin:0 0 6px;font-size:26px}} header p{{margin:2px 0;opacity:.85;font-size:14px}} |
| .wrap{{max-width:1180px;margin:0 auto;padding:24px 20px 60px}} |
| .module{{margin:34px 0}} |
| .module h2{{border-left:7px solid var(--accent);padding:6px 0 6px 14px;font-size:21px;margin:0 0 16px;color:var(--accent)}} |
| .card{{background:#fff;border-radius:12px;box-shadow:0 1px 4px rgba(0,0,0,.08);margin:0 0 20px;overflow:hidden}} |
| .card-h{{padding:14px 20px;border-bottom:1px solid #eef0f3;display:flex;align-items:baseline;gap:14px;flex-wrap:wrap}} |
| .card-h h3{{margin:0;font-size:17px}} .card-h .meta{{color:#788196;font-size:12.5px}} |
| .bg{{margin:14px 20px 4px;padding:10px 14px;background:#f8fafc;border:1px solid #eef0f3;border-radius:8px;font-size:12.5px;color:#475569;line-height:1.8}} |
| .bg b{{color:#334155}} |
| h4.sec{{margin:16px 20px 2px;font-size:13.5px;color:#334155;border-left:3px solid var(--accent);padding-left:9px}} |
| .content{{display:flex;gap:18px;padding:8px 20px 14px;flex-wrap:wrap;align-items:flex-start}} |
| .tbl{{flex:1 1 440px;overflow-x:auto}} .chart{{flex:1 1 480px;text-align:center}} .chart img{{max-width:100%;height:auto}} |
| table.metrics{{border-collapse:collapse;width:100%;font-size:13px}} |
| table.metrics th,table.metrics td{{padding:6px 8px;text-align:center;border-bottom:1px solid #eef0f3}} |
| table.metrics thead th{{background:#f8f9fb;color:#5b6472;font-weight:600;font-size:11.5px}} |
| table.metrics td.model{{text-align:left;white-space:nowrap;font-weight:600}} |
| .dot{{display:inline-block;width:9px;height:9px;border-radius:50%;margin-right:7px;vertical-align:middle}} |
| table.metrics td.best{{background:#fff7e6;color:#b45309;font-weight:700}} |
| table.metrics .grp{{border-left:2px solid #e2e6ea}} |
| details{{border-top:1px solid #eef0f3;padding:10px 20px 16px}} summary{{cursor:pointer;color:#475569;font-size:13px;font-weight:600}} |
| .grow h4{{margin:14px 0 8px;font-size:13px;color:#5b6472}} |
| .imgrow{{display:flex;gap:12px;flex-wrap:wrap}} |
| .imgrow figure{{margin:0;flex:1 1 300px;max-width:360px;text-align:center}} |
| .imgrow img{{width:100%;border:1px solid #eef0f3;border-radius:6px}} .imgrow figcaption{{font-size:11.5px;color:#788196;margin-top:4px}} |
| footer{{text-align:center;color:#9aa3b2;font-size:12px;padding:20px}} |
| </style></head><body> |
| <header> |
| <h1>眼底图像分类 Benchmark · RetFound vs ResNet vs ViT</h1> |
| <p>7 个数据集 · 4 个疾病方向 · 三模型（RetFound ViT-L / ResNet-50 / ViT-B/16，均预训练后全参数微调）</p> |
| <p>每个数据集含：采集背景（FOV/设备/来源/分辨率）· 类别分布（按 split）· 模型性能（指标表 + 柱状图）· 混淆矩阵/ROC</p> |
| <p>评估协议：输入 224 · 官方划分优先（否则 7:1:2 分层）· val 选最优→测 test · 指标由统一脚本计算</p> |
| </header> |
| <div class="wrap"> |
| {''.join(parts)} |
| <footer>采集背景均据权威来源整理，不确定处已标注「设备标称/未公开/补充材料」· 单文件离线可用</footer> |
| </div></body></html>""" |
| open(OUT, "w").write(html) |
| print(f"wrote {OUT} ({len(html)/1024:.0f} KB)") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
