We elaborate on biases which are typically inherent in histopathological image data. This work shows how heatmaps generated by these explanation methods allow to resolve common challenges encountered in deep learning-based digital histopathology analyses. Recently, many explanation methods have emerged. However, the medical domain requires explanation and insight for a better understanding beyond standard quantitative performance evaluation. Deep learning has recently gained popularity in digital pathology due to its high prediction quality. ![]() loadToNCHW ( img, mean, input_size ) # submit the image to net and get a tensor of results results = p. SerializeToString ()) # use whatever image you want (urls work too) img = "images/flower.jpg" # average mean to subtract from the image mean = 128 # the size of images that the model was trained with input_size = 227 # use the image helper to load the image and convert it to NCHW img = helpers. predict_net # you must name it something predict_net. download - i squeezenet # load up the caffe2 workspace from caffe2.python import workspace # choose your model here (use the downloader first) from import squeezenet as mynet # helper image processing functions import helpers # load the pre-trained model init_net = mynet. shape ) + " in HWC" ) return imgScaled 加载均值 resize ( img, ( input_height, input_width )) print ( "New image shape:" + str ( imgScaled. resize ( img, ( res, input_width )) if ( aspect = 1 ): imgScaled = skimage. resize ( img, ( input_height, res )) if ( aspect < 1 ): # portrait orientation - tall image res = int ( input_width / aspect ) imgScaled = skimage. shape ) print ( "Orginal aspect ratio: " + str ( aspect )) if ( aspect > 1 ): # landscape orientation - wide image res = int ( aspect * input_height ) imgScaled = skimage. shape ) + " and remember it should be in H, W, C!" ) print ( "Model's input shape is %d x %d " ) % ( input_height, input_width ) aspect = img. shape startx = x // 2 - ( cropx // 2 ) starty = y // 2 - ( cropy // 2 ) return img def rescale ( img, input_height, input_width ): print ( "Original image shape:" + str ( img. # The list of output codes for the AlexNet models (squeezenet) codes = "" print "Config set!" crop_center和rescale函数ĭef crop_center ( img, cropx, cropy ): y, x, c = img. # format below is the model's: # folder, INIT_NET, predict_net, mean, input image size # you can switch squeezenet out with 'bvlc_alexnet', 'bvlc_googlenet' or others that you have downloaded # if you have a mean file, place it in the same dir as the model MODEL = 'squeezenet', 'init_net.pb', 'predict_net.pb', 'ilsvrc_2012_mean.npy', 227 # codes - these help decypher the output and source from a list from AlexNet's object codes to provide an result like "tabby cat" or "lemon" depending on what's in the picture you submit to the neural network. IMAGE_LOCATION = "images/flower.jpg" # What model are we using? You should have already converted or downloaded one. ![]() ![]() insert ( 0, '/usr/local' ) from caffe2.proto import caffe2_pb2 import numpy as np import skimage.io import ansform from matplotlib import pyplot import os from caffe2.python import core, workspace, models import urllib2 print ( "Required modules imported." ) # Configuration - Change to your setup and preferences! CAFFE_MODELS = "/usr/local/caffe2/python/models" # sample images you can try, or use any URL to a regular image.
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