深度学习模型分析工具箱
BioExp的Python项目详细描述
生物经验
解释在医学图像和自然图像中执行各种图像处理任务的深度学习模型。在
特点
- [x] 解剖分析
- [x] 烧蚀分析
- [x] 不确定性分析
- [x] 基于Bayesian-Dropout的认知不确定性
- [x] 利用测试时间增加的任意不确定度
- [x] 活化最大化
- [x] 凸轮分析
- [x] 输入与概念空间的RCT
- [x] 概念生成聚类分析
- [x] 基于wts的聚类
- [x] 基于特征的聚类
- [x] 概念识别
- [x] 基于解剖的
- [x] 基于流的
- [x] 因果图
- [x] 推理方法
- []视觉轨迹上的反事实
- []反事实生成
- []事前方法（元因果关系）
引用
如果您使用BioExp，请引用我们的工作：
@article{natekar2019demystifying,
title={Demystifying Brain Tumour Segmentation Networks: Interpretability and Uncertainty Analysis},
author={Natekar, Parth and Kori, Avinash and Krishnamurthi, Ganapathy},
journal={arXiv preprint arXiv:1909.01498},
year={2019}
}
定义的管道
安装
运行可解释性管道需要一个GPU和几个深度学习模块。在
要求
- “熊猫”
- “纽比”
- 'scipy==1.6.0'
- 'matplotlib'
- “枕头”
- “简单点”
- “opencv python”
- 'tensorflow gpu==1.14'
- “克拉斯”
- “keras-vis”
- “清醒”
下面的命令将只安装上面列出的依赖项。在
^{pr2}$烧蚀
使用
from BioExp.spatial import Ablation
A = spatial.Ablation(model = model,
weights_pth = weights_path,
metric = dice_label_coef,
layer_name = layer_name,
test_image = test_image,
gt = gt,
classes = infoclasses,
nclasses = 4)
df = A.ablate_filter(step = 1)
解剖
使用
from BioExp.spatial import Dissector
layer_name = 'conv2d_3'
infoclasses = {}
for i in range(1): infoclasses['class_'+str(i)] = (i,)
infoclasses['whole'] = (1,2,3)
dissector = Dissector(model=model,
layer_name = layer_name)
threshold_maps = dissector.get_threshold_maps(dataset_path = data_root_path,
save_path = savepath,
percentile = 85)
dissector.apply_threshold(image, threshold_maps,
nfeatures =9,
save_path = savepath,
ROI = ROI)
dissector.quantify_gt_features(image, gt,
threshold_maps,
nclasses = infoclass,
nfeatures = 9,
save_path = savepath,
save_fmaps = False,
ROI = ROI)
结果
格拉德卡姆
使用
from BioExp.spatial import cam
dice = flow.cam(model, img, gt,
nclasses = nclasses,
save_path = save_path,
layer_idx = -1,
threshol = 0.5,
modifier = 'guided')
结果
活化最大化
使用
from BioExp.concept.feature import Feature_Visualizer
class Load_Model(Model):
model_path = '../../saved_models/model_flair_scaled/model.pb'
image_shape = [None, 1, 240, 240]
image_value_range = (0, 10)
input_name = 'input_1'
E = Feature_Visualizer(Load_Model, savepath = '../results/', regularizer_params={'L1':1e-3, 'rotate':8})
a = E.run(layer = 'conv2d_17', class_ = 'None', channel = 95, transforms=True)
激活结果
不确定性
使用
from BioExp.uncertainty import uncertainty
D = uncertainty(test_image)
# for aleatoric
mean, var = D.aleatoric(model, iterations = 50)
# for epistemic
mean, var = D.epistemic(model, iterations = 50)
# for combined
mean, var = D.combined(model, iterations = 50)
结果
无线电技术
使用
from BioExp.helpers import radfeatures
feat_extractor = radfeatures.ExtractRadiomicFeatures(image, mask, save_path = pth)
df = feat_extractor.all_features()
因果推理管道
联系人
- 阿维纳什可丽（koriavinash1@gmail.com）
- 帕斯·纳泰卡（parth@smail.iitm.ac.in）
- 项目
标签：
欢迎加入QQ群-->： 979659372
