Upload Opth_proj1 backup

.gitattributes

@@ -58,3 +58,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+manifests/eyepacs_augmented_resized_v2_clean/labels.csv filter=lfs diff=lfs merge=lfs -text

DATA_USE.md

@@ -0,0 +1,7 @@
+# Data Use Notes
+
+This repository contains third-party public ophthalmic imaging datasets and derived experiment artifacts used for research on clinician-confirmed online adaptation.
+
+Users must cite and comply with the original dataset sources and licenses, including but not limited to EyePACS/Kaggle diabetic retinopathy data, APTOS2019, DeepDRiD, IDRiD, and Messidor2. IDRiD and DeepDRiD include Creative Commons licensing in their original releases; other datasets may have separate data-use terms.
+
+This repository does not contain private hospital data.

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code/OAIMS_code_snapshot.tar

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configs/label_mapping_dr.yaml

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+classes:
+  0: grade_0_no_dr
+  1: grade_1_mild
+  2: grade_2_moderate
+  3: grade_3_severe
+  4: grade_4_proliferative
+referable_dr:
+  negative: [0, 1]
+  positive: [2, 3, 4]

configs/source_target_matrix.yaml

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+primary_task: referable_diabetic_retinopathy
+sources:
+  - aptos2019
+  - eyepacs_augmented_resized_v2_clean
+  - deepdrid
+targets:
+  - aptos2019
+  - deepdrid
+  - idrid-dataset
+  - messidor2
+methods:
+  - source_only
+  - naive_online_head
+  - safe_delayed_update
+  - safe_source_replay
+  - batch_plus_replay

dataset_inventory.tsv

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+name	path	bytes	role
+aptos2019	datasets/aptos2019.tar	8606085120	DR grading/referable source or target
+deepdrid	datasets/deepdrid.tar	1103943680	DR grading/referable source or target
+idrid-dataset	datasets/idrid-dataset.tar	187217920	DR grading/referable external target
+messidor2	datasets/messidor2_preprocess.tar	403496960	DR grading/referable external target
+eyepacs_augmented_resized_v2_clean	datasets/eyepacs_augmented_resized_v2_clean.tar	3917639680	large DR source training set used in experiments
+idrid_annotations	annotations/idrid_annotations.tar	1009930240	lesion/structure annotations for future click-guided adaptation

datasets/aptos2019.tar

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docs/eyepacs_README.md

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+# EyePACS / APTOS / Messidor DR Combination Dataset
+
+本目录为 Kaggle 数据集 `ascanipek/eyepacs-aptos-messidor-diabetic-retinopathy` 的本地副本。
+
+## 本地路径
+
+`/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/Data/Generation/EyePACS`
+
+## 模态与任务
+
+- 模态：彩色眼底照相图像，Color Fundus Photography，简称 CFP。
+- 主要任务：糖尿病视网膜病变，Diabetic Retinopathy，简称 DR，五分类分级。
+- 标签形式：目录名 `0-4` 表示 DR 严重程度等级。
+- 来源组成：EyePACS/Kaggle Diabetic Retinopathy Detection、APTOS 2019、APTOS Gaussian Filtered、Messidor。
+
+## 文件结构
+
+```text
+EyePACS/
+├── augmented_resized_V2/
+│   ├── train/0..4/
+│   ├── val/0..4/
+│   └── test/0..4/
+└── dr_unified_v2/
+    └── dr_unified_v2/
+        ├── train/0..4/
+        ├── val/0..4/
+        └── test/0..4/
+```
+
+说明：`4.archive` 源压缩包已在清理时删除，数据已经解压到上述目录。
+
+## 数据量
+
+### `dr_unified_v2/dr_unified_v2`
+
+这是原始统一数据集，共 92,501 张 JPG，随机划分为 train/validation/test = 80:10:10。
+
+| split | 0 | 1 | 2 | 3 | 4 | 合计 |
+|---|---:|---:|---:|---:|---:|---:|
+| train | 55,162 | 3,706 | 12,119 | 1,006 | 2,002 | 73,995 |
+| val | 6,895 | 462 | 1,514 | 125 | 249 | 9,245 |
+| test | 6,896 | 466 | 1,518 | 128 | 253 | 9,261 |
+| 合计 | 68,953 | 4,634 | 15,151 | 1,259 | 2,504 | 92,501 |
+
+### `augmented_resized_V2`
+
+这是 2024-01-05 更新后的增强版本。数据集页面说明为：手工数据增强后样本数增加约 55%，所有图像 resize 到 600 x 600，磁盘体积从约 18.5 GB 降到约 3.8 GB。当前本地统计为 143,669 张。
+
+| split | 0 | 1 | 2 | 3 | 4 | 合计 |
+|---|---:|---:|---:|---:|---:|---:|
+| train | 55,162 | 18,470 | 24,198 | 7,936 | 9,475 | 115,241 |
+| val | 6,895 | 1,840 | 3,024 | 1,000 | 1,468 | 14,227 |
+| test | 6,896 | 1,862 | 2,999 | 978 | 1,466 | 14,201 |
+| 合计 | 68,953 | 22,172 | 30,221 | 9,914 | 12,409 | 143,669 |
+
+## 标签说明
+
+DR 分级标签通常按如下语义使用：
+
+| 标签 | 含义 |
+|---:|---|
+| 0 | No DR |
+| 1 | Mild DR |
+| 2 | Moderate DR |
+| 3 | Severe DR |
+| 4 | Proliferative DR |
+
+## 来源与引用提示
+
+该组合集不主张拥有原始图像版权。使用前应核对并遵守原始数据集的许可证、使用条款和引用要求：
+
+- EyePACS / Kaggle Diabetic Retinopathy Detection。
+- APTOS 2019 Blindness Detection。
+- APTOS Gaussian Filtered，派生自 APTOS 2019。
+- Messidor。推荐引用：Decencière et al., "Feedback on a publicly distributed database: the Messidor database", Image Analysis & Stereology, 2014.
+
+Messidor 官方要求致谢：`Kindly provided by the Messidor program partners.`
+
+## 按接入手册的整理建议
+
+- 该数据集属于 A 类：疾病分级标签。
+- 推荐 cohort：`public_eyepacs_combo_dr`。如果确实要接入增强版，建议另设 `public_eyepacs_combo_dr_aug600`，不要和原始统一版混在同一 cohort。
+- `modality=fundus_color`，`scan_protocol=single_shot`，`has_segmentation=False`，`SEGMENTATION_TYPE=none`。
+- `diagnosis_group=["normal"]` 对应 0 类，`diagnosis_group=["DR"]` 对应 1-4 类。
+- `severity` 映射为 `0->none, 1->mild, 2->moderate, 3->severe, 4->proliferative`。
+- 因为该组合集已经包含 APTOS、Messidor 等公开来源，若同时接入单独的 APTOS2019、Messidor2，应先做文件哈希或感知哈希去重，避免训练/验证/测试泄漏。
+- 对严格评估，优先使用 `dr_unified_v2`；`augmented_resized_V2` 更适合作为预训练或数据增强版训练源，不建议作为独立泛化测试集。

docs/messidor2_README.md

@@ -0,0 +1,73 @@
+# Messidor-2 Dataset
+
+本目录为 Messidor-2 diabetic retinopathy 数据集的本地副本。
+
+## 本地路径
+
+`/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/Data/Generation/Messidor2`
+
+## 模态与任务
+
+- 模态：彩色眼底照相图像，Color Fundus Photography，简称 CFP。
+- 主要任务：糖尿病视网膜病变，Diabetic Retinopathy，简称 DR，五分类分级。
+- 辅助标签：糖尿病黄斑水肿，Diabetic Macular Edema，简称 DME，以及图像可分级性。
+
+## 文件结构
+
+```text
+Messidor2/
+├── messidor-2/
+│   └── messidor-2/
+│       └── preprocess/
+├── messidor_data.csv
+└── .complete/
+```
+
+## 数据量
+
+- 图像数量：1,744 张。
+- 标注行数：1,744 行。
+- 主要标注文件：`messidor_data.csv`。
+
+`messidor_data.csv` 字段：
+
+| 字段 | 含义 |
+|---|---|
+| `id_code` | 图像文件名 |
+| `diagnosis` | DR 分级，0-4 |
+| `adjudicated_dme` | DME 标签，0/1 |
+| `adjudicated_gradable` | 图像是否可分级 |
+
+## 标签分布
+
+DR 标签：
+
+| diagnosis | 数量 |
+|---:|---:|
+| 0 | 1,017 |
+| 1 | 270 |
+| 2 | 347 |
+| 3 | 75 |
+| 4 | 35 |
+
+DME 标签：
+
+| adjudicated_dme | 数量 |
+|---:|---:|
+| 0 | 1,593 |
+| 1 | 151 |
+
+可分级性：
+
+| adjudicated_gradable | 数量 |
+|---:|---:|
+| 1 | 1,744 |
+
+## 按接入手册的整理建议
+
+- 该数据集属于 A 类：疾病分级标签。
+- 推荐 cohort：`public_messidor2_dr`。
+- `diagnosis` 按 DR 0-4 映射到 `severity`。
+- `adjudicated_dme=1` 可转成 `lesion_tags=["DME"]`，也可保存到 cohort sidecar 中，具体取决于后续训练是否使用 DME 标签。
+- `adjudicated_gradable=1` 可作为质量过滤依据；当前本地 CSV 中全部样本均为可分级。
+- 该数据源也可能出现在 EyePACS 组合集中；如果两者都接入，需要做图像去重或 split 隔离。

docs/refine-logs/EXPERIMENT_PLAN.md

@@ -0,0 +1,82 @@
+# Experiment Plan
+
+**Problem**: Medical image classifiers degrade under external dataset shift after deployment.
+**Method Thesis**: Clinician-confirmed labels can drive safe online adaptation when updates are delayed and constrained with source replay.
+**Date**: 2026-07-03
+
+## Claim Map
+
+| Claim | Why It Matters | Minimum Convincing Evidence | Linked Blocks |
+|---|---|---|---|
+| C1: Safe label-only online adaptation improves or preserves referable DR performance under external shift. | This is the main clinically realistic contribution. | Positive paired delta on at least two external targets, with bootstrap CI not crossing zero for Macro F1 or balanced accuracy. | B1, B2 |
+| C2: Safety mechanisms prevent catastrophic drift. | Naive online learning can harm patients if it drifts. | Naive adaptation degrades on at least one target while batch/replay avoids the degradation. | B2, B3 |
+
+## Paper Storyline
+
+- Main paper must prove: APTOS/EyePACS source models can adapt safely to DeepDRiD, IDRiD, and Messidor2.
+- Appendix can support: 5-class grading, glaucoma transfer, lesion-click simulation.
+- Experiments intentionally cut for now: real clinician clicking, until label-only evidence is statistically stable.
+
+## Experiment Blocks
+
+### Block 1: External Referable DR Validation
+- Claim tested: C1.
+- Dataset / split / task: source APTOS and EyePACS; targets DeepDRiD, IDRiD, Messidor2; referable DR binary classification.
+- Compared systems: source-only, naive per-sample update, batch-only, replay-only, batch+replay.
+- Metrics: primary Macro F1; secondary accuracy, AUROC, sensitivity, specificity.
+- Setup details: ResNet50, bf16, DDP for source training, 5 shuffled arrival-order seeds.
+- Success criterion: batch+replay has positive Macro F1 delta on most targets and no large AUC collapse.
+- Table / figure target: Main Table 1 and adaptation curve figure.
+- Priority: MUST-RUN.
+
+### Block 2: Paired Statistical Testing
+- Claim tested: C1.
+- Why this block exists: aggregate metrics are insufficient for medical-paper claims.
+- Dataset / split / task: same as Block 1.
+- Compared systems: source-only vs fixed batch+replay protocol.
+- Metrics: paired bootstrap CI for delta metrics; McNemar test for accuracy; bootstrap/DeLong-style AUROC CI.
+- Success criterion: primary endpoint CI excludes zero on key targets, or consistent positive CI trend with transparent target-level reporting.
+- Table / figure target: Main Table 2 / supplement.
+- Priority: MUST-RUN.
+
+### Block 3: Safety Ablations
+- Claim tested: C2.
+- Dataset / split / task: DeepDRiD and IDRiD referable DR.
+- Compared systems: naive, low-lr, batch-only, replay-only, replay sizes 2/4/8/16.
+- Metrics: Macro F1 delta, AUC delta, worst-target degradation.
+- Success criterion: batch/replay reduces negative drift versus naive.
+- Table / figure target: Main ablation table.
+- Priority: MUST-RUN.
+
+### Block 4: Lesion-Click Simulation
+- Claim tested: supporting OAIMS-style mechanism.
+- Dataset / split / task: IDRiD disease grading plus lesion masks.
+- Compared systems: label-only vs label + lesion-region attention constraint.
+- Metrics: classification metrics plus saliency/click alignment.
+- Priority: NICE-TO-HAVE after Blocks 1-3.
+
+## Run Order and Milestones
+
+| Milestone | Goal | Runs | Decision Gate | Cost | Risk |
+|---|---|---|---|---|---|
+| M0 | Add per-sample prediction logging and stats scripts. | Smoke test on small DeepDRiD subset. | CSV and bootstrap JSON are valid. | Low | Column mismatch. |
+| M1 | Add Messidor2 external target. | Build derived labels.csv under Code/Opth/Dataset. | Image paths resolve and source-only runs. | Low | Label convention mismatch. |
+| M2 | APTOS external validation. | batch+replay8 on DeepDRiD, IDRiD, Messidor2, 5 seeds. | Positive or non-negative deltas on most targets. | Medium | Messidor2 shift may be harder. |
+| M3 | Statistical testing. | Bootstrap/McNemar on prediction CSVs. | CI supports primary claim. | Low | Need stricter endpoint. |
+| M4 | Strong-source validation. | Train EyePACS referable model, test same targets. | Reproducible trend with stronger source. | High | EyePACS training time. |
+
+## Compute and Data Budget
+
+- Total estimated GPU-hours for immediate APTOS external validation: low to moderate; online evaluation is inference plus small head updates.
+- Total estimated GPU-hours for EyePACS source model: moderate to high; use DDP bf16.
+- Data preparation needs: derived labels only; original datasets remain read-only.
+- Human evaluation needs: none until lesion-click or clinical reader study.
+- Biggest bottleneck: making target-level statistical evidence strong enough for medical-review standards.
+
+## Final Checklist
+
+- [ ] Main paper tables are covered
+- [ ] Novelty is isolated
+- [ ] Simplicity is defended
+- [ ] Frontier contribution is explicitly not claimed
+- [ ] Nice-to-have lesion-click runs are separated from must-run label-only runs

docs/refine-logs/EXPERIMENT_TRACKER.md

@@ -0,0 +1,13 @@
+# Experiment Tracker
+
+| Run ID | Milestone | Purpose | System / Variant | Split | Metrics | Priority | Status | Notes |
+|---|---|---|---|---|---|---|---|---|
+| R001 | M0 | Smoke per-sample logging | APTOS->DeepDRiD batch+replay8 | target test subset | CSV + JSON | MUST | TODO | Verify new prediction columns. |
+| R002 | M1 | Prepare external target | Messidor2 derived labels | all gradable as test | row count | MUST | TODO | Write to Code/Opth/Dataset only. |
+| R003 | M2 | External validation | APTOS->Messidor2 batch+replay8 | test, seeds 0-4 | Acc/F1/AUC | MUST | TODO | Adds third DR target. |
+| R004 | M3 | Statistical test | APTOS->DeepDRiD safe | predictions CSVs | CI/McNemar | MUST | TODO | Re-run because old logs lack per-sample rows. |
+| R005 | M3 | Statistical test | APTOS->IDRiD safe | predictions CSVs | CI/McNemar | MUST | TODO | Re-run because old logs lack per-sample rows. |
+| R006 | M3 | Statistical test | APTOS->Messidor2 safe | predictions CSVs | CI/McNemar | MUST | TODO | New external validation. |
+| R007 | M4 | Strong source | EyePACS referable DDP training | train/val | F1/AUC | NICE | TODO | Launch after APTOS evidence is clean. |
+| R008 | M5 | Safety ablation sweep | APTOS/EyePACS -> DeepDRiD/IDRiD/Messidor2 | 15 variants x 5 seeds | Delta Acc/F1/AUC + CI | MUST | RUNNING | `overnight_20260703_dr_ablation`, 450 tasks on GPU 0/1/2/3/4/5/7. |
+| R009 | M6 | Third source validation | DeepDRiD source -> APTOS/IDRiD/Messidor2 | selected variants x 5 seeds | Delta Acc/F1/AUC + CI | NICE | QUEUED | `overnight_controller` trains DeepDRiD source after R008 finishes. |

docs/refine-logs/RESULTS_SUMMARY_20260704.md

@@ -0,0 +1,79 @@
+# Results Summary: DR Online Adaptation
+
+**Date**: 2026-07-04
+
+## Completed Runs
+
+- Primary ablation sweep: `overnight_20260703_dr_ablation`
+  - 2 source models: APTOS, EyePACS
+  - 3 external targets: DeepDRiD, IDRiD, Messidor2
+  - 15 online adaptation variants
+  - 5 shuffled arrival-order seeds
+  - 450 seed runs, 90 bootstrap summaries, no failed tasks
+- Follow-up source validation: `overnight_20260703_deepdrid_source`
+  - DeepDRiD source model
+  - 3 external targets: APTOS, IDRiD, Messidor2
+  - 7 key variants
+  - 105 seed runs, 21 bootstrap summaries, no failed tasks
+
+## Main Evidence
+
+Best Macro F1 gains by transfer:
+
+| Source -> Target | Best Variant | Delta Macro F1 | 95% CI | Interpretation |
+|---|---|---:|---|---|
+| APTOS -> DeepDRiD | batch-only | +0.1099 | [+0.0916, +0.1276] | Strong improvement |
+| APTOS -> IDRiD | replay-only | +0.0300 | [+0.0107, +0.0489] | Significant improvement |
+| APTOS -> Messidor2 | batch + small replay | +0.1323 | [+0.1208, +0.1450] | Strong improvement |
+| EyePACS -> DeepDRiD | safe lr3e-6 | +0.0019 | [-0.0055, +0.0094] | Preserves strong baseline |
+| EyePACS -> IDRiD | safe steps2 | +0.0314 | [+0.0153, +0.0491] | Significant improvement |
+| EyePACS -> Messidor2 | batch4 + replay | +0.0261 | [+0.0199, +0.0328] | Significant improvement |
+| DeepDRiD -> APTOS | safe lr3e-6 | +0.0213 | [+0.0117, +0.0318] | Significant improvement |
+| DeepDRiD -> IDRiD | safe lr3e-6 | +0.0239 | [+0.0021, +0.0458] | Significant improvement |
+| DeepDRiD -> Messidor2 | replay-only | -0.0037 | [-0.0096, +0.0022] | No reliable gain |
+
+## Safety Finding
+
+Naive per-case updates frequently drift:
+
+| Transfer | Naive Delta F1 | Safe/Best Delta F1 |
+|---|---:|---:|
+| APTOS -> IDRiD | -0.2217 | +0.0300 |
+| DeepDRiD -> IDRiD | -0.2288 | +0.0239 |
+| EyePACS -> IDRiD | -0.1499 | +0.0314 |
+| EyePACS -> Messidor2 | -0.0375 | +0.0261 |
+
+This supports the claim that clinician-confirmed online learning needs delayed updates and/or source replay to be clinically safe.
+
+## Generated Paper Assets
+
+Output directory:
+
+`/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/Code/Opth/figures/dr_online_adaptation`
+
+Key files:
+
+- `table_best_online_adaptation.tsv`
+- `table_safety_ablation.tsv`
+- `table_variant_summary.tsv`
+- `table_best_online_adaptation.tex`
+- `table_safety_ablation.tex`
+- `table_variant_summary.tex`
+- `fig_safety_ablation_delta_f1.pdf`
+- `fig_best_delta_f1_heatmap.pdf`
+- `fig_variant_mean_delta_f1.pdf`
+- `fig_adaptation_curves.pdf`
+- `latex_includes.tex`
+
+## Recommended Paper Position
+
+Use referable DR as the main task and frame the method as:
+
+> A clinically realistic, label-confirmed online adaptation framework that improves external-domain DR classification when domain shift is present, while safety mechanisms prevent the catastrophic drift caused by naive per-case fine-tuning.
+
+Avoid claiming universal improvement. The strongest, defensible claim is:
+
+- Online adaptation improves or preserves external performance in most source-target transfers.
+- Naive online fine-tuning can be unsafe.
+- Delayed updates and source replay are necessary safety mechanisms.
+

figures/dr_online_adaptation/latex_includes.tex

@@ -0,0 +1,27 @@
+% === Figure: safety ablation ===
+\begin{figure}[t]
+\centering
+\includegraphics[width=0.95\textwidth]{figures/dr_online_adaptation/fig_safety_ablation_delta_f1.pdf}
+\caption{Safety ablation for label-only online adaptation. Naive per-case updates often drift, whereas delayed batch updates and source replay stabilize Macro F1 across external transfers.}
+\label{fig:safety_ablation}
+\end{figure}
+
+% === Figure: best external transfer deltas ===
+\begin{figure}[t]
+\centering
+\includegraphics[width=0.55\textwidth]{figures/dr_online_adaptation/fig_best_delta_f1_heatmap.pdf}
+\caption{Best observed Macro F1 improvement for each source-target transfer after online adaptation.}
+\label{fig:best_delta_heatmap}
+\end{figure}
+
+% === Figure: adaptation curves ===
+\begin{figure}[t]
+\centering
+\includegraphics[width=0.95\textwidth]{figures/dr_online_adaptation/fig_adaptation_curves.pdf}
+\caption{Representative online adaptation curves under shuffled target-domain arrival order.}
+\label{fig:adaptation_curves}
+\end{figure}
+
+\input{figures/dr_online_adaptation/table_best_online_adaptation.tex}
+\input{figures/dr_online_adaptation/table_safety_ablation.tex}
+\input{figures/dr_online_adaptation/table_variant_summary.tex}

figures/dr_online_adaptation/table_best_online_adaptation.tex

@@ -0,0 +1,20 @@
+\begin{table}[t]
+\centering
+\caption{Best online adaptation configuration for each source-target transfer, selected by Macro F1 improvement.}
+\label{tab:best_online_adaptation}
+\begin{tabular}{llrrrrl}
+\toprule
+Source & Target & Source F1 & Online F1 & $\Delta$F1 & 95\% CI & Variant \\
+\midrule
+aptos & deepdrid & 0.6236 & 0.7335 & +0.1099 & [+0.0916, +0.1276] & Batch \\
+aptos & idrid & 0.8537 & 0.8837 & +0.0300 & [+0.0107, +0.0489] & Replay \\
+aptos & messidor2 & 0.5653 & 0.6976 & +0.1323 & [+0.1208, +0.1450] & Batch+Replay2 \\
+deepdrid & aptos & 0.8635 & 0.8847 & +0.0213 & [+0.0117, +0.0318] & Batch+Replay lr3e-6 \\
+deepdrid & idrid & 0.8896 & 0.9135 & +0.0239 & [+0.0021, +0.0458] & Batch+Replay lr3e-6 \\
+deepdrid & messidor2 & 0.7725 & 0.7688 & -0.0037 & [-0.0096, +0.0022] & Replay \\
+eyepacs & deepdrid & 0.9113 & 0.9132 & +0.0019 & [-0.0055, +0.0094] & Batch+Replay lr3e-6 \\
+eyepacs & idrid & 0.9067 & 0.9381 & +0.0314 & [+0.0153, +0.0491] & Batch+Replay steps2 \\
+eyepacs & messidor2 & 0.8380 & 0.8640 & +0.0261 & [+0.0199, +0.0328] & Batch4+Replay \\
+\bottomrule
+\end{tabular}
+\end{table}

figures/dr_online_adaptation/table_best_online_adaptation.tsv

@@ -0,0 +1,10 @@
+source	target	chosen_variant	source_macro_f1	online_macro_f1	delta_macro_f1	delta_macro_f1_ci	delta_acc	delta_auc	mcnemar_p	sig
+aptos	deepdrid	batch8_r0_lr1e-6	0.6236164715639054	0.7335086819229575	0.10989221035905206	[+0.0916, +0.1276]	0.0645	-0.008154195948739118	5.757580012928986e-19	+
+aptos	idrid	replay8_u1_lr1e-6	0.8537003058103976	0.8837457073622763	0.030045401551878737	[+0.0107, +0.0489]	0.030434782608695587	0.001099947117927047	0.003650434404441877	+
+aptos	messidor2	safe_b8_r2_lr1e-6	0.5652731286945374	0.6976013455352974	0.13232821684076002	[+0.1208, +0.1450]	0.019266055045871533	0.011615770565442274	6.31936758230094e-07	+
+deepdrid	aptos	safe_b8_r8_lr3e-6	0.8634692246203037	0.8847341850170725	0.02126496039676873	[+0.0117, +0.0318]	0.021857923497267784	-0.0010684346412520584	2.0881663011151364e-05	+
+deepdrid	idrid	safe_b8_r8_lr3e-6	0.8896441423430628	0.9134958941891808	0.023851751846117986	[+0.0021, +0.0458]	0.019565217391304346	-0.003278688524590123	0.0808555983700523	+
+deepdrid	messidor2	replay8_u1_lr1e-6	0.772489327804331	0.768764790636026	-0.003724537168305009	[-0.0096, +0.0022]	-0.006995412844036708	-0.008547280582291594	0.0016372747184498818	0
+eyepacs	deepdrid	safe_b8_r8_lr3e-6	0.9112711709595762	0.913187267465895	0.001916096506318743	[-0.0055, +0.0094]	0.0010000000000000009	-0.0052066969822239795	0.8936946693232327	0
+eyepacs	idrid	safe_b8_r8_steps2_lr1e-6	0.9066937119675456	0.9380644626729637	0.03137075070541817	[+0.0153, +0.0491]	0.0304347826086957	-0.00499206768905347	0.0011540500847815347	+
+eyepacs	messidor2	safe_b4_r8_lr1e-6	0.8379564443618535	0.8640118839739437	0.026055439612090225	[+0.0199, +0.0328]	0.011926605504587129	0.00805346853486899	2.6048645906640325e-07	+

figures/dr_online_adaptation/table_safety_ablation.tex

@@ -0,0 +1,20 @@
+\begin{table}[t]
+\centering
+\caption{Safety ablation showing that naive per-sample updates often drift, while delayed or replay-constrained updates recover performance. Values are $\Delta$ Macro F1.}
+\label{tab:safety_ablation}
+\begin{tabular}{llrrrr}
+\toprule
+Source & Target & Naive & Batch & Replay & Batch+Replay \\
+\midrule
+aptos & deepdrid & +0.0061 & +0.1099 & +0.0140 & +0.0622 \\
+aptos & idrid & -0.2217 & -0.0380 & +0.0300 & +0.0085 \\
+aptos & messidor2 & +0.0646 & +0.1264 & +0.0458 & +0.1037 \\
+deepdrid & aptos & -0.0449 & +0.0105 & +0.0003 & +0.0165 \\
+deepdrid & idrid & -0.2288 & +0.0131 & +0.0208 & +0.0210 \\
+deepdrid & messidor2 & -0.0275 & -0.0181 & -0.0037 & -0.0054 \\
+eyepacs & deepdrid & -0.1124 & +0.0001 & -0.0072 & +0.0010 \\
+eyepacs & idrid & -0.1499 & +0.0134 & +0.0202 & +0.0202 \\
+eyepacs & messidor2 & -0.0375 & +0.0040 & +0.0122 & +0.0183 \\
+\bottomrule
+\end{tabular}
+\end{table}

figures/dr_online_adaptation/table_safety_ablation.tsv

@@ -0,0 +1,10 @@
+source	target	naive_delta_macro_f1	batch_delta_macro_f1	replay_delta_macro_f1	default_safe_delta_macro_f1	best_variant	best_delta_macro_f1	best_ci
+aptos	deepdrid	0.006059457277478364	0.10989221035905206	0.014024115709981633	0.06223051044446626	batch8_r0_lr1e-6	0.10989221035905206	[+0.0916, +0.1276]
+aptos	idrid	-0.22171946893841843	-0.03804589827047933	0.030045401551878737	0.008533896046445832	replay8_u1_lr1e-6	0.030045401551878737	[+0.0107, +0.0489]
+aptos	messidor2	0.06459959838291107	0.12639411354631025	0.045780843093882306	0.1036917808174368	safe_b8_r2_lr1e-6	0.13232821684076002	[+0.1208, +0.1450]
+deepdrid	aptos	-0.04488335277156419	0.010501641225036717	0.0002751621439947005	0.016492669212087918	safe_b8_r8_lr3e-6	0.02126496039676873	[+0.0117, +0.0318]
+deepdrid	idrid	-0.2287809880301268	0.013094743021872635	0.020772666874215973	0.021047186530084305	safe_b8_r8_lr3e-6	0.023851751846117986	[+0.0021, +0.0458]
+deepdrid	messidor2	-0.027510929665414352	-0.018118114258692142	-0.003724537168305009	-0.005397892305541108	replay8_u1_lr1e-6	-0.003724537168305009	[-0.0096, +0.0022]
+eyepacs	deepdrid	-0.11238316209257104	0.00013835498867664775	-0.007227402333645827	0.000979737759689625	safe_b8_r8_lr3e-6	0.001916096506318743	[-0.0055, +0.0094]
+eyepacs	idrid	-0.14993038897153277	0.013397845531790242	0.020162703996836617	0.020162703996836617	safe_b8_r8_steps2_lr1e-6	0.03137075070541817	[+0.0153, +0.0491]
+eyepacs	messidor2	-0.03745214818477027	0.004007972463406451	0.01220185607055857	0.01834133281794159	safe_b4_r8_lr1e-6	0.026055439612090225	[+0.0199, +0.0328]

figures/dr_online_adaptation/table_variant_summary.tex

@@ -0,0 +1,21 @@
+\begin{table}[t]
+\centering
+\caption{Variant-level summary across source-target transfers. Positive/negative counts indicate how many transfers have a 95\% bootstrap CI above or below zero.}
+\label{tab:variant_summary}
+\begin{tabular}{lrrrr}
+\toprule
+Variant & Mean $\Delta$F1 & Median $\Delta$F1 & Positive & Negative \\
+\midrule
+Batch+Replay lr3e-6 & +0.0281 & +0.0202 & 6/9 & 0/9 \\
+Batch+Replay & +0.0273 & +0.0183 & 6/9 & 0/9 \\
+Batch+Replay lr3e-7 & +0.0270 & +0.0186 & 6/9 & 0/9 \\
+Full lr1e-7 & +0.0383 & +0.0216 & 5/6 & 0/6 \\
+Batch4+Replay & +0.0350 & +0.0248 & 5/6 & 0/6 \\
+Batch+Replay16 & +0.0326 & +0.0227 & 5/6 & 0/6 \\
+BN+Head & +0.0264 & +0.0184 & 5/9 & 0/9 \\
+Replay & +0.0147 & +0.0140 & 5/9 & 1/9 \\
+Batch+Replay steps2 & +0.0398 & +0.0232 & 4/6 & 0/6 \\
+Batch+Replay lr1e-5 & +0.0376 & +0.0201 & 4/6 & 0/6 \\
+\bottomrule
+\end{tabular}
+\end{table}

figures/dr_online_adaptation/table_variant_summary.tsv

@@ -0,0 +1,16 @@
+variant	mean_delta_macro_f1	median_delta_macro_f1	positive_ci_count	negative_ci_count	num_pairs
+safe_b8_r8_lr3e-6	0.028054048557712034	0.020162703996836617	6	0	9
+safe_b8_r8_lr1e-6	0.027342436146605316	0.01834133281794159	6	0	9
+safe_b8_r8_lr3e-7	0.026988382252481506	0.018607549751416874	6	0	9
+full_b8_r8_lr1e-7	0.03834701655047305	0.021632597786558072	5	0	6
+safe_b4_r8_lr1e-6	0.03499900667785901	0.024809079273365897	5	0	6
+safe_b8_r16_lr1e-6	0.032646707150784327	0.022682636873942508	5	0	6
+bnhead_b8_r8_lr1e-6	0.02639280815088417	0.01843128293400731	5	0	9
+replay8_u1_lr1e-6	0.014701201104377523	0.014024115709981633	5	1	9
+safe_b8_r8_steps2_lr1e-6	0.039751965666117196	0.023164932592437792	4	0	6
+safe_b8_r8_lr1e-5	0.037623241564937425	0.020143996107183648	4	0	6
+safe_b8_r8_lr1e-7	0.03527014254347153	0.01897334068152734	4	0	6
+safe_b8_r2_lr1e-6	0.03763512282541189	0.012107759482857305	3	0	6
+safe_b16_r8_lr1e-6	0.03511091775158515	0.012973546601892982	3	0	6
+batch8_r0_lr1e-6	0.024584763178552613	0.010501641225036717	2	2	9
+naive_u1_r0_lr1e-6	-0.08355570922155649	-0.04488335277156419	1	7	9

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manifest_summary.tsv

@@ -0,0 +1,6 @@
+dataset	manifest	n	splits	labels
+aptos2019	manifests/aptos2019/labels.csv	3662	{'test': 366, 'train': 2930, 'val': 366}	{'0': 1805, '1': 370, '2': 999, '3': 193, '4': 295}
+deepdrid	manifests/deepdrid/labels.csv	2000	{'test': 400, 'train': 1200, 'val': 400}	{'0': 913, '1': 223, '2': 398, '3': 354, '4': 112}
+eyepacs_augmented_resized_v2_clean	manifests/eyepacs_augmented_resized_v2_clean/labels.csv	143668	{'train': 115240, 'val': 14227, 'test': 14201}	{'0': 68953, '1': 22172, '2': 30220, '3': 9914, '4': 12409}
+idrid-dataset	manifests/idrid-dataset/labels.csv	455	{'test': 92, 'train': 318, 'val': 45}	{'0': 129, '1': 22, '2': 156, '3': 84, '4': 64}
+messidor2	manifests/messidor2/labels.csv	1744	{'test': 1744}	{'0': 1017, '1': 270, '2': 347, '3': 75, '4': 35}

manifests/deepdrid/labels.csv

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