diff --git a/.gitattributes b/.gitattributes index 1ef325f1b111266a6b26e0196871bd78baa8c2f3..ec2abcb57139d9428a4bff1dde039a72aded2b78 100644 --- a/.gitattributes +++ b/.gitattributes @@ -57,3 +57,12 @@ 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 +gliomasam3_moe/vis_res/ampmix/Fig8_a_ampmix_BraTS-GLI-00005-000.pdf filter=lfs diff=lfs merge=lfs -text +gliomasam3_moe/vis_res/boundary/Fig3_a_boundary_BraTS-GLI-00005-000.pdf filter=lfs diff=lfs merge=lfs -text +gliomasam3_moe/vis_res/boundary/Fig3_b_boundary_BraTS-GLI-00017-000.pdf filter=lfs diff=lfs merge=lfs -text +gliomasam3_moe/vis_res/concept_tokens/Fig6_b1_et_overview_BraTS-GLI-00006-000.pdf filter=lfs diff=lfs merge=lfs -text +gliomasam3_moe/vis_res/dual_domain/Fig7_a_dual_domain_BraTS-GLI-00005-000.pdf filter=lfs diff=lfs merge=lfs -text +gliomasam3_moe/vis_res/dual_domain/Fig7_b_dual_domain_BraTS-GLI-00017-000.pdf filter=lfs diff=lfs merge=lfs -text +gliomasam3_moe/vis_res/et_absent/Fig2_a_et_absent_BraTS-GLI-00012-000.pdf filter=lfs diff=lfs merge=lfs -text +gliomasam3_moe/vis_res/failure/Fig9_a_failure_BraTS-GLI-00020-000.pdf filter=lfs diff=lfs merge=lfs -text +gliomasam3_moe/vis_res/qualitative/Fig1_qualitative_comparison.pdf filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ce7496dfa246201f966eb965eed8707d93d7adbf --- /dev/null +++ b/README.md @@ -0,0 +1,97 @@ +# SegMamba & GliomaSAM3-MoE Model Weights and Results + +This repository contains pre-trained model weights, evaluation results, and visualization outputs for brain tumor segmentation on BraTS 2023 dataset. + +## Repository Structure + +``` +. +├── gliomasam3_moe/ +│ ├── checkpoints/ # GliomaSAM3-MoE model weights +│ │ ├── ckpt_step2000.pt +│ │ ├── ckpt_step2600.pt +│ │ └── ckpt_step3000.pt # Best checkpoint +│ ├── configs/ +│ │ └── train.yaml # Training configuration +│ ├── eval_results/ +│ │ ├── table4_et_absent.json # ET presence classification results +│ │ └── table7_boundary_dice.json # Boundary-band Dice results +│ └── vis_res/ # Visualization results +│ ├── method_comparison/ # Side-by-side comparisons +│ ├── boundary/ # Boundary analysis figures +│ ├── moe_routing/ # MoE routing visualizations +│ └── ... +│ +├── segmamba/ +│ ├── checkpoints/ # SegMamba model weights +│ │ ├── tmp_model_ep599_0.8295.pt +│ │ └── tmp_model_ep799_0.8498.pt # Best checkpoint (Dice=0.8498) +│ └── prediction_results/ +│ ├── segmamba_brats23_ep799/ # Prediction NIfTI files +│ └── result_metrics/ # Evaluation metrics +│ +└── README.md +``` + +## Model Performance + +### GliomaSAM3-MoE (ckpt_step3000) + +**Boundary-band Dice (3-voxel band):** +| Region | Dice | +|--------|------| +| WT | 0.789 ± 0.057 | +| TC | 0.766 ± 0.154 | +| ET | 0.697 ± 0.161 | +| **Mean** | **0.750** | + +**ET Presence Classification:** +| Metric | Value | +|--------|-------| +| AUROC | 0.896 | +| Accuracy | 0.795 | +| Sensitivity | 0.792 | +| Specificity | 1.000 | + +### SegMamba (ep799) + +- Mean Dice: 0.8498 +- Trained for 800 epochs on BraTS 2023 + +## Usage + +### Loading GliomaSAM3-MoE + +```python +import torch + +# Load checkpoint +ckpt = torch.load("gliomasam3_moe/checkpoints/ckpt_step3000.pt", map_location="cpu") + +# Model state dict is in ckpt["model"] +model.load_state_dict(ckpt["model"]) +``` + +### Loading SegMamba + +```python +import torch + +# Load checkpoint +ckpt = torch.load("segmamba/checkpoints/tmp_model_ep799_0.8498.pt", map_location="cpu") + +# Model state dict +model.load_state_dict(ckpt["model"]) +``` + +## Data + +Models were trained and evaluated on BraTS 2023 GLI Challenge dataset (122 cases in processed subset). + +## Citation + +If you use these models, please cite the relevant papers. + +## License + +Please refer to the original model repositories for licensing information. diff --git a/gliomasam3_moe/checkpoints/ckpt_step2000.pt b/gliomasam3_moe/checkpoints/ckpt_step2000.pt new file mode 100644 index 0000000000000000000000000000000000000000..97a9725b0c1b0fb354aef84a9528dcae72c8f062 --- /dev/null +++ b/gliomasam3_moe/checkpoints/ckpt_step2000.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:886d389ca84282d411b5a8643d578bc540ef32477c05b498a66d2ef8c62e6507 +size 1821693123 diff --git a/gliomasam3_moe/checkpoints/ckpt_step2600.pt b/gliomasam3_moe/checkpoints/ckpt_step2600.pt new file mode 100644 index 0000000000000000000000000000000000000000..a8f77484da32622511f779d7c54ee76e391ea9b0 --- /dev/null +++ b/gliomasam3_moe/checkpoints/ckpt_step2600.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5886858a63de35d717697158e747060ca9df3104a2d92637cb040966a8af96e2 +size 1821693123 diff --git a/gliomasam3_moe/checkpoints/ckpt_step3000.pt b/gliomasam3_moe/checkpoints/ckpt_step3000.pt new file mode 100644 index 0000000000000000000000000000000000000000..aa8e44c5a3f45146fcec8204d63b31430cb933e8 --- /dev/null +++ b/gliomasam3_moe/checkpoints/ckpt_step3000.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ffe568c3046807bb3b15bd36427ac935cd495acac15cd4937988b92f85cbab1a +size 1821693123 diff --git a/gliomasam3_moe/configs/train.yaml b/gliomasam3_moe/configs/train.yaml new file mode 100644 index 0000000000000000000000000000000000000000..aad3bb8e069bca6a4f50b662598a12f28fc2d6f3 --- /dev/null +++ b/gliomasam3_moe/configs/train.yaml @@ -0,0 +1,76 @@ +seed: 42 +device: "cuda" +amp: true +synthetic: false + +data: + format: "segmamba_npz" + root_dir: "/data/yty/brats23_segmamba_processed" + modalities: ["t1n", "t1c", "t2f", "t2w"] + seg_name: "seg" + orientation: "RAS" + do_spacing: false + spacing: [1.0, 1.0, 1.0] + crop_size: [128, 128, 128] + num_samples: 1 + rand_scale_prob: 0.1 + rand_shift_prob: 0.1 + batch_size: 6 + synthetic_shape: [16, 128, 128] + synthetic_cases: 16 + train_rate: 0.7 + val_rate: 0.1 + test_rate: 0.2 + segmamba_unpack: true + +model: + patch_size: 14 + token_dim: 128 + depth: 3 + heads: 4 + mlp_ratio: 4.0 + slice_attn_k: 6 + slice_attn_random_dir: true + spectral_bins: 24 + spectral_q: 3 + msda_scales: [3, 5, 7] + moe_experts: 5 + moe_topk: 2 + decoder_hidden: 96 + prompt_mlp_hidden: 128 + use_sam3_backbone: true + sam3_ckpt_path: "/data/yty/sam3/sam3.pt" + sam3_freeze: true + sam3_in_chans: 7 + sam3_input_mean: [0.5, 0.5, 0.5] + sam3_input_std: [0.5, 0.5, 0.5] + +loss: + dice_weight: 1.0 + bce_weight: 1.0 + et_focal_weight: 0.5 + focal_gamma: 2.0 + pres_weight: 0.1 + hier_weight: 0.1 + moe_weight: 0.01 + +train: + epochs: 300 + max_steps: 10000 + lr: 0.0002 + weight_decay: 0.00001 + log_every: 20 + save_every: 200 + ckpt_dir: "./logs/segmamba/model" + fourier_mix_prob: 0.2 + num_workers: 4 + use_label_prompt: true + test_every_epochs: 5 + test_max_cases: 0 + +infer: + roi_size: [128, 128, 128] + sw_batch_size: 1 + overlap: 0.5 + threshold: 0.5 + et_cc_min_size: 50 diff --git a/gliomasam3_moe/eval_results/table4_et_absent.json b/gliomasam3_moe/eval_results/table4_et_absent.json new file mode 100644 index 0000000000000000000000000000000000000000..c425dc0d7deb784b13c016e068afae987877a34a --- /dev/null +++ b/gliomasam3_moe/eval_results/table4_et_absent.json @@ -0,0 +1,43 @@ +{ + "et_absent_subset": { + "n": 2, + "fp_volume_mm3": { + "mean": 74.5, + "std": 74.5, + "min": 0.0, + "max": 149.0 + }, + "fp_components": { + "mean": 0.5, + "std": 0.5, + "min": 0, + "max": 1 + } + }, + "et_absent_cases": [ + { + "case_id": "BraTS-GLI-00017-001", + "fp_volume_mm3": 149, + "fp_components": 1 + }, + { + "case_id": "BraTS-GLI-00048-001", + "fp_volume_mm3": 0, + "fp_components": 0 + } + ], + "classification": { + "n": 122, + "n_et_present": 120, + "n_et_absent": 2, + "auroc": 0.8958333333333333, + "optimal_threshold": 0.9947388768196106, + "accuracy_optimal": 0.7950819672131147, + "sensitivity_optimal": 0.7916666666666666, + "specificity_optimal": 1.0 + }, + "config": { + "min_size": 50, + "threshold": 0.5 + } +} \ No newline at end of file diff --git a/gliomasam3_moe/eval_results/table7_boundary_dice.json b/gliomasam3_moe/eval_results/table7_boundary_dice.json new file mode 100644 index 0000000000000000000000000000000000000000..14a9105785d06f1a2b07653d6c08a1db272625c8 --- /dev/null +++ b/gliomasam3_moe/eval_results/table7_boundary_dice.json @@ -0,0 +1,26 @@ +{ + "stats": { + "WT": { + "mean": 0.7889820841743286, + "std": 0.05687755328186895, + "n": 122 + }, + "TC": { + "mean": 0.7656578316169296, + "std": 0.1538429439512892, + "n": 122 + }, + "ET": { + "mean": 0.6964991356739063, + "std": 0.1612273062938224, + "n": 122 + }, + "Mean": { + "mean": 0.7503796838217216 + } + }, + "config": { + "radius": 3, + "min_size": 50 + } +} \ No newline at end of file diff --git a/gliomasam3_moe/vis_res/README.md b/gliomasam3_moe/vis_res/README.md new file mode 100644 index 0000000000000000000000000000000000000000..9f45b01e902490467f186b3df0ff26c67a9c97c8 --- /dev/null +++ b/gliomasam3_moe/vis_res/README.md @@ -0,0 +1,206 @@ +# GliomaSAM3-MoE 可视化结果总结 + +本文档总结了各可视化实验的内容、目的和主要结论。 + +--- + +## 1. 主定性对比 (qualitative/) + +**文件**: `Fig1_qualitative_comparison.png` + +**内容**: +- 多模态输入（T1, T1ce, T2, FLAIR） +- Ground Truth 与预测结果对比 +- 我们的方法 (GliomaSAM3-MoE) vs SegMamba +- WT/TC/ET 三区域分割叠加显示 + +**结论**: 展示模型在不同病例上的定性分割效果，通过彩色叠加可以直观比较各方法在肿瘤边界、区域完整性上的差异。 + +--- + +## 2. ET Gate 研究 (et_absent/) + +**文件**: `Fig2_a_et_absent_BraTS-GLI-00012-000.png` + +**内容**: +- ET Before Gate: 经过 gate 前的 ET 预测概率图 +- ET After Gate: 经过 gate 后的 ET 预测概率图 +- π_ET 值: 模型预测的 ET 存在概率 + +**结论**: +- π_ET 值反映模型对 ET（增强肿瘤）存在性的判断置信度 +- 当 π_ET > 0.5 时，模型认为存在 ET；反之则抑制 ET 预测 +- 此机制可帮助减少 ET 假阳性，但在当前训练下效果较弱（大多数 π_ET 接近 0.97-0.98） + +--- + +## 3. 边界误差分析 (boundary/) + +**文件**: +- `Fig3_a_boundary_BraTS-GLI-00005-000.png` +- `Fig3_b_boundary_BraTS-GLI-00017-000.png` + +**内容**: +- 左侧: T1ce 图像 + Ground Truth 边界（白色）+ 预测边界（黑色） +- 右侧: 边界误差热力图（红色=假阳性FP，蓝色=假阴性FN） + +**结论**: +- 边界误差热力图直观展示分割误差的空间分布 +- 可用于分析 HD95 指标的改善区域 +- 误差主要集中在肿瘤边缘模糊区域 + +--- + +## 4. 微小/碎片 ET 分析 (tiny_et/) + +**文件**: +- `Fig4_a_tiny_et_BraTS-GLI-00005-000.png` +- `Fig4_b_tiny_et_BraTS-GLI-00006-000.png` + +**内容**: +- 局部 ROI 放大显示 +- Ground Truth vs 我们的方法 vs SegMamba +- 专注于 ET 极小或碎片化区域 + +**结论**: 展示模型对微小 ET 区域的捕获能力，验证是否能保留碎片化结构而不丢失或过度平滑。 + +--- + +## 5. MoE 路由可解释性 (moe_routing/) + +**文件**: +- `Fig5_a_moe_routing_BraTS-GLI-00018-000.png` +- `Fig5_b_moe_routing_BraTS-GLI-00012-000.png` + +**内容**: +- 柱状图展示各专家对 WT/TC/ET 三个区域的贡献权重 +- X 轴: 专家索引 (E0-E4)，激活的专家用 "(active)" 标注 +- Y 轴: 贡献值 +- 颜色区分: WT(青色), TC(品红), ET(黄色) +- 黑色折线: 路由权重 γ，激活专家标注具体值 +- 非激活专家显示为半透明（alpha=0.3） + +**重要说明**: +- 模型配置 `moe_topk: 2`，即采用 **Top-2 稀疏门控** +- 每次推理**只激活 2 个专家**，其余专家权重为 0 +- 这是 Mixture-of-Experts 的标准设计，不是 bug + +**结论**: +- Top-2 稀疏门控使计算效率提高，同时保持模型容量 +- 不同案例可能激活不同的专家组合（如 E0+E2 或其他） +- 路由权重 γ 显示各激活专家的相对重要性 + +--- + +## 6. 概念 Token 可解释性 (concept_tokens/) + +每个病例生成 3 张图： + +### 6.1 ET 预测概览 (Fig6_X1_et_overview_*.png) +- 左: T1ce 原始输入 +- 右: ET 预测 mask 叠加 + 体素总数 + +### 6.2 碎片化分析 (Fig6_X2_fragmentation_*.png) +- 左: 连通域可视化（不同颜色标识各组件） +- 中: 各组件大小柱状图 +- 右: FRAG_BIN 分类（None/Low/Medium/High） + +### 6.3 规模分析 (Fig6_X3_scale_*.png) +- 左: ET 区域可视化 +- 右: SCALE_BIN 分类（Tiny/Small/Medium/Large） + +**结论**: +- **FRAG_BIN**: 根据 ET 连通域数量判断碎片化程度 + - n ≤ 1 → None, n ≤ 3 → Low, n ≤ 5 → Medium, n > 5 → High +- **SCALE_BIN**: 根据 ET 体素总数判断规模 + - voxels ≤ 50 → Tiny, ≤ 200 → Small, ≤ 500 → Medium, > 500 → Large +- 这些概念 token 与肿瘤形态特征直接关联，可用于辅助临床决策 + +--- + +## 7. 双域增强效果 (dual_domain/) + +**文件**: +- `Fig7_a_dual_domain_BraTS-GLI-00005-000.png` +- `Fig7_b_dual_domain_BraTS-GLI-00017-000.png` + +**内容**: +- 左: 原始图像的傅里叶幅度谱 +- 右: 增强后的傅里叶幅度谱 +- 统一色标便于比较 + +**结论**: +- 频域可视化展示模型如何利用频谱信息 +- 增强后的幅度谱可能显示高频成分增强，有助于边界检测 +- 体现双域（空域+频域）融合的效果 + +--- + +## 8. AmpMix 增强鲁棒性 (ampmix/) + +**文件**: `Fig8_a_ampmix_BraTS-GLI-00005-000.png` + +**内容**: +- 左: 原始图像 + 预测 +- 中: AmpMix 扰动后的图像 +- 右: 扰动后的预测结果 + +**结论**: +- AmpMix 通过混合不同样本的傅里叶幅度谱进行数据增强 +- 展示模型在幅度谱扰动下的预测稳定性 +- 理想情况下，扰动前后预测应保持一致，边界不发生显著漂移 + +--- + +## 9. 失败案例分析 (failure/) + +**文件**: `Fig9_a_failure_BraTS-GLI-00020-000.png` + +**内容**: +- 左: Ground Truth +- 右: 预测结果 +- 标注: 失败原因说明 + +**结论**: +- 展示模型的局限性 +- 典型失败原因包括: + - 边界模糊区域的判断困难 + - 低对比度区域的误分割 + - 伪影或异常强度影响 +- 诚实展示模型限制有助于后续改进方向 + +--- + +## 文件结构 + +``` +vis_res/ +├── README.md # 本文档 +├── qualitative/ # 主定性对比 +├── et_absent/ # ET Gate 研究 +├── boundary/ # 边界误差分析 +├── tiny_et/ # 微小 ET 分析 +├── moe_routing/ # MoE 路由可解释性 +├── concept_tokens/ # 概念 Token 可解释性 +├── dual_domain/ # 双域增强效果 +├── ampmix/ # AmpMix 鲁棒性 +├── failure/ # 失败案例 +└── aux_cache/ # 中间结果缓存（内部使用） +``` + +--- + +## 生成方式 + +所有可视化由 `visualizations/vis_publication.py` 生成，配置文件为 `visualizations/vis_config.yaml`。 + +```bash +cd /root/githubs/gliomasam3_moe +PYTHONPATH=/root/githubs/sam3:$PYTHONPATH python visualizations/vis_publication.py \ + --config visualizations/vis_config.yaml \ + --model-config configs/train.yaml \ + --checkpoint ./logs/segmamba/model/ckpt_step600.pt \ + --run all +``` + +可通过 `--run ` 单独生成特定可视化（如 `--run moe,dual_domain`）。 diff --git a/gliomasam3_moe/vis_res/ampmix/Fig8_a_ampmix_BraTS-GLI-00005-000.pdf b/gliomasam3_moe/vis_res/ampmix/Fig8_a_ampmix_BraTS-GLI-00005-000.pdf new file mode 100644 index 0000000000000000000000000000000000000000..ab5817475fb42c5d0ad48b78817133b01af7d3da --- /dev/null +++ b/gliomasam3_moe/vis_res/ampmix/Fig8_a_ampmix_BraTS-GLI-00005-000.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a4e155669c0f2ae5ba5340a49a6a131afdba8873a179db1efb67b7fdf3c022e1 +size 132112 diff --git a/gliomasam3_moe/vis_res/ampmix/Fig8_a_ampmix_BraTS-GLI-00005-000.png b/gliomasam3_moe/vis_res/ampmix/Fig8_a_ampmix_BraTS-GLI-00005-000.png new file mode 100644 index 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sha256:32aeb79f903050c3e0c2730926d0977f3b906296bb2987c00f1b266814cefaf9 +size 90546 diff --git a/gliomasam3_moe/vis_res/method_comparison/README.md b/gliomasam3_moe/vis_res/method_comparison/README.md new file mode 100644 index 0000000000000000000000000000000000000000..4a6e3a85cc5865a5462213cd988225e2fa1c8a9d --- /dev/null +++ b/gliomasam3_moe/vis_res/method_comparison/README.md @@ -0,0 +1,76 @@ +# Method Comparison Visualization + +本文件夹包含 GliomaSAM3-MoE 和 SegMamba 两种方法在不同 checkpoint 下的可视化对比结果。 + +## 文件结构 + +``` +method_comparison/ +├── README.md # 本说明文件 +├── comparison_grid.png # 总体对比网格图 +├── BraTS-GLI-00005-000/ # Case 1 +│ ├── input_T1.png # 原始 T1 图像 +│ ├── input_T1ce.png # 原始 T1ce 图像 +│ ├── input_FLAIR.png # 原始 FLAIR 图像 +│ ├── input_T2.png # 原始 T2 图像 +│ ├── gt_overlay.png # Ground Truth (WT+TC+ET 叠加) +│ ├── gt_WT.png # Ground Truth - 全肿瘤区域 +│ ├── gt_TC.png # Ground Truth - 肿瘤核心区域 +│ ├── gt_ET.png # Ground Truth - 增强肿瘤区域 +│ ├── pred_gliomasam_step2000_overlay.png # GliomaSAM3-MoE step2000 预测 +│ ├── pred_gliomasam_step2600_overlay.png # GliomaSAM3-MoE step2600 预测 +│ ├── pred_gliomasam_step3000_overlay.png # GliomaSAM3-MoE step3000 预测 +│ ├── pred_segmamba_segmamba_default_overlay.png # SegMamba 默认预测 +│ └── pred_segmamba_segmamba_ep799_overlay.png # SegMamba ep799 预测 +├── BraTS-GLI-00006-000/ # Case 2 +├── BraTS-GLI-00012-000/ # Case 3 (无 SegMamba 预测) +├── BraTS-GLI-00017-000/ # Case 4 +├── BraTS-GLI-00018-000/ # Case 5 (无 SegMamba 预测) +└── BraTS-GLI-00020-000/ # Case 6 +``` + +## 方法和 Checkpoint + +### GliomaSAM3-MoE (3 checkpoints) +- `step2000`: /root/githubs/gliomasam3_moe/logs/segmamba/model/ckpt_step2000.pt +- `step2600`: /root/githubs/gliomasam3_moe/logs/segmamba/model/ckpt_step2600.pt +- `step3000`: /root/githubs/gliomasam3_moe/logs/segmamba/model/ckpt_step3000.pt + +### SegMamba (2 预生成结果) +- `segmamba_default`: /root/githubs/SegMamba/prediction_results/segmamba_brats23/ +- `segmamba_ep799`: /root/githubs/SegMamba/prediction_results/segmamba_brats23_ep799/ + +## 颜色编码 + +- **WT (全肿瘤)**: 青色 (#00BBD4) +- **TC (肿瘤核心)**: 品红色 (#D81B60) +- **ET (增强肿瘤)**: 黄色 (#FBC02D) + +## 选择的案例 + +共选择 6 个案例进行可视化: +1. BraTS-GLI-00005-000 +2. BraTS-GLI-00006-000 +3. BraTS-GLI-00012-000 (无 SegMamba 预测) +4. BraTS-GLI-00017-000 +5. BraTS-GLI-00018-000 (无 SegMamba 预测) +6. BraTS-GLI-00020-000 + +## 文件命名规则 + +- `input_{modality}.png`: 原始输入图像 +- `gt_overlay.png`: Ground Truth 叠加显示 +- `gt_{region}.png`: Ground Truth 单区域显示 (WT/TC/ET) +- `pred_gliomasam_{checkpoint}_overlay.png`: GliomaSAM3-MoE 预测叠加 +- `pred_gliomasam_{checkpoint}_{region}.png`: GliomaSAM3-MoE 预测单区域 +- `pred_segmamba_{name}_overlay.png`: SegMamba 预测叠加 +- `pred_segmamba_{name}_{region}.png`: SegMamba 预测单区域 + +## 生成方式 + +```bash +cd /root/githubs/gliomasam3_moe +PYTHONPATH=/root/githubs/sam3:$PYTHONPATH python visualizations/vis_method_comparison.py +``` + +可以通过 `--cases` 参数指定特定的 case ID。 diff --git a/gliomasam3_moe/vis_res/method_comparison/comparison_grid.png b/gliomasam3_moe/vis_res/method_comparison/comparison_grid.png new file mode 100644 index 0000000000000000000000000000000000000000..2861f22b2c38b931270dd11c9e5708c791e4f55c --- /dev/null +++ b/gliomasam3_moe/vis_res/method_comparison/comparison_grid.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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