模型概述
模型特點
模型能力
使用案例
🚀 Phikon-v2 模型卡片
Phikon-v2 是一個基於視覺變換器(Vision Transformer)的大型預訓練模型。它採用 Dinov2 自監督方法,在 PANCAN-XL 數據集上進行預訓練。PANCAN-XL 數據集包含 4.5 億張 20 倍放大的組織學圖像,這些圖像從 6 萬張全切片圖像(WSI)中採樣得到。PANCAN-XL 僅整合了公開可用的數據集,包括用於惡性組織的 CPTAC(6,193 張 WSI)和 TCGA(29,502 張 WSI),以及用於正常組織的 GTEx(13,302 張 WSI)。
與我們之前基於 iBOT 在來自 TCGA(6k WSI)的 4000 萬張組織學圖像上預訓練的基礎模型 Phikon 相比,Phikon-v2 在各種為生物標誌物發現量身定製的弱監督任務上表現更優。為避免與 PANCAN-XL 預訓練數據集發生數據汙染,Phikon-v2 在外部隊列上進行評估,並與一系列表徵學習和基礎模型進行了基準對比。
🚀 快速開始
模型描述
- 開發者:Owkin, Inc
- 模型類型:預訓練視覺骨幹網絡(通過 DINOv2 實現的 ViT-L/16)
- 預訓練數據集:PANCAN-XL,源自公共組織學數據集(TCGA、CPTAC、GTEx、TCIA 等)
- 論文:Arxiv
- 許可證:Owkin 非商業許可證
如何使用(特徵提取)
以下代碼片段展示瞭如何使用 Phikon-v2(CLS 標記)從組織學圖像中提取特徵。這些特徵可用於下游應用,如感興趣區域(ROI)分類(通過線性或 KNN 探測)、切片分類(通過多實例學習)、分割(例如通過 ViT-Adapter)等。
from PIL import Image
import torch
from transformers import AutoImageProcessor, AutoModel
# Load an image
image = Image.open(
requests.get(
"https://github.com/owkin/HistoSSLscaling/blob/main/assets/example.tif?raw=true",
stream=True
).raw
)
# Load phikon-v2
processor = AutoImageProcessor.from_pretrained("owkin/phikon-v2")
model = AutoModel.from_pretrained("owkin/phikon-v2")
model.eval()
# Process the image
inputs = processor(image, return_tensors="pt")
# Get the features
with torch.inference_mode():
outputs = model(**inputs)
features = outputs.last_hidden_state[:, 0, :] # (1, 1024) shape
assert features.shape == (1, 1024)
直接使用(使用預提取和凍結的特徵)
Phikon-v2 可以在不同的下游應用中進行微調或不進行微調使用。例如,可以使用多實例學習算法(如 ABMIL)進行切片分類。
下游使用(微調)
你可以在切片級別的下游任務上微調該模型。這個 Colab 筆記本 允許你通過 Hugging Face API 使用 LoRA 微調 Phikon 和 Phikon-v2。
✨ 主要特性
- 強大的特徵提取能力:基於 Vision Transformer 架構和 Dinov2 自監督方法,能夠從組織學圖像中提取高質量特徵。
- 廣泛的數據集支持:在大規模的 PANCAN-XL 數據集上預訓練,該數據集整合了多個公開可用的組織學數據集。
- 優秀的下游任務表現:在各種弱監督任務上優於之前的模型,適用於生物標誌物發現等應用。
📦 安裝指南
軟件依賴
Python 包
- torch>==2.0.0:https://pytorch.org
- torchvision>=0.15.0:https://pytorch.org/vision/stable/index.html
- xformers>=0.0.18:https://github.com/facebookresearch/xformers
代碼倉庫
- DINOv2(自監督學習):https://github.com/facebookresearch/dinov2
💻 使用示例
基礎用法
from PIL import Image
import torch
from transformers import AutoImageProcessor, AutoModel
# Load an image
image = Image.open(
requests.get(
"https://github.com/owkin/HistoSSLscaling/blob/main/assets/example.tif?raw=true",
stream=True
).raw
)
# Load phikon-v2
processor = AutoImageProcessor.from_pretrained("owkin/phikon-v2")
model = AutoModel.from_pretrained("owkin/phikon-v2")
model.eval()
# Process the image
inputs = processor(image, return_tensors="pt")
# Get the features
with torch.inference_mode():
outputs = model(**inputs)
features = outputs.last_hidden_state[:, 0, :] # (1, 1024) shape
assert features.shape == (1, 1024)
高級用法
你可以使用 這個 Colab 筆記本 通過 Hugging Face API 使用 LoRA 微調 Phikon 和 Phikon-v2。
📚 詳細文檔
訓練詳情
- 訓練數據:PANCAN-XL,一個由 4.56 億張 [224×224]、20 倍分辨率的組織學圖像組成的預訓練數據集,這些圖像從 6 萬張 H&E WSI 中採樣得到。
- 訓練機制:使用 PyTorch-FSDP 混合精度的 fp16。
- 訓練目標:採用 DINOv2 自監督學習方法,包含以下損失函數:
- 具有多裁剪的 DINO 自蒸餾損失
- iBOT 掩碼圖像建模損失
- 對 [CLS] 標記的 KoLeo 正則化
- 訓練時長:10 萬次迭代,批次大小為 4,096
- 模型架構:ViT-Large(0.3B 參數):補丁大小 16,嵌入維度 1024,16 個頭,多層感知機前饋網絡(MLP FFN)
- 使用的硬件:32×4 塊 Nvidia V100 32GB GPU
- 訓練總時長:約 4300 GPU 小時(總計 33 小時)
- 訓練平臺:法國超級集群 Jean-Zay
第三方許可證
視覺變換器架構源自 facebookresearch/dino(Apache 許可證 2.0)和 huggingface/pytorch-image-models(Apache 許可證 2.0)。此代碼基於 DINOv2 代碼倉庫(Apache 許可證 2.0)構建。
屬性 | 詳情 |
---|---|
模型類型 | 預訓練視覺骨幹網絡(通過 DINOv2 實現的 ViT-L/16) |
預訓練數據集 | PANCAN-XL,源自公共組織學數據集(TCGA、CPTAC、GTEx、TCIA 等) |
論文 | Arxiv |
許可證 | Owkin 非商業許可證 |
預訓練數據集許可證
🔧 技術細節
Phikon-v2 基於 Vision Transformer 架構,利用 Dinov2 自監督學習方法在大規模組織學數據集上進行預訓練。通過結合多種損失函數,如 DINO 自蒸餾損失、iBOT 掩碼圖像建模損失和 KoLeo 正則化,模型能夠學習到魯棒的視覺特徵。在訓練過程中,採用了 PyTorch-FSDP 混合精度訓練,以提高訓練效率。
📄 許可證
本模型使用 Owkin 非商業許可證。
聯繫信息
如有任何額外問題或建議,請聯繫 Alexandre Filiot (alexandre.filiot@owkin.com
)。
如何引用
@misc{filiot2024phikonv2largepublicfeature,
title={Phikon-v2, A large and public feature extractor for biomarker prediction},
author={Alexandre Filiot and Paul Jacob and Alice Mac Kain and Charlie Saillard},
year={2024},
eprint={2409.09173},
archivePrefix={arXiv},
primaryClass={eess.IV},
url={https://arxiv.org/abs/2409.09173},
}
致謝
我們感謝 DINOv2 的作者們做出的傑出貢獻 [1]。
計算資源
本研究獲得了 IDRIS 高性能計算資源的支持,該支持由 GENCI 分配(2023 - A0141012519)。
數據集
本研究部分結果基於 TCGA 研究網絡生成的數據:https://www.cancer.gov/tcga。基因型 - 組織表達(GTEx)項目由美國國立衛生研究院院長辦公室共同基金以及 NCI、NHGRI、NHLBI、NIDA、NIMH 和 NINDS 支持。本研究中分析使用的數據於 2023 年 7 月 1 日從 GTEx 門戶獲取。
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