Implementation of paper [arXiv]:
"IMPaSh: A Novel Domain-shift Resistant Representation for Colorectal Cancer Tissue Classification" by Trinh Thi Le Vuong, Quoc Dang Vu, Mostafa Jahanifar, Simon Graham, Jin Tae Kwak, and Nasir Rajpoot.
(Will be present at ECCV Workshop 2022)
Note: The clean version will be release later.
IMPaSh's encoder and classifier weights:
import numpy as np
from random import shuffle
from PIL import Image
class PatchShuffling(object):
"""
PatchShuffling Module
"""
def __init__(self, n_grid=3, img_size=255, crop_size=64):
self.n_grid = n_grid
self.img_size = img_size
self.crop_size = crop_size
self.grid_size = int(img_size / self.n_grid)
self.side = self.grid_size - self.crop_size
yy, xx = np.meshgrid(np.arange(n_grid), np.arange(n_grid))
self.yy = np.reshape(yy * self.grid_size, (n_grid * n_grid,))
self.xx = np.reshape(xx * self.grid_size, (n_grid * n_grid,))
self.re_yy = np.reshape(yy * self.crop_size, (n_grid * n_grid,))
self.re_xx = np.reshape(xx * self.crop_size, (n_grid * n_grid,))
def __call__(self, img):
r_x = np.random.randint(0, self.side + 1, self.n_grid * self.n_grid)
r_y = np.random.randint(0, self.side + 1, self.n_grid * self.n_grid)
img = np.asarray(img, np.uint8)
crops = []
for i in range(self.n_grid * self.n_grid):
crops.append(img[self.xx[i] + r_x[i]: self.xx[i] + r_x[i] + self.crop_size,
self.yy[i] + r_y[i]: self.yy[i] + r_y[i] + self.crop_size, :])
shuffle(crops)
shuffling_img = np.zeros([self.crop_size*self.n_grid, self.crop_size*self.n_grid, 3], dtype='uint8')
for i in range(self.n_grid * self.n_grid):
shuffling_img[self.re_xx[i]: self.re_xx[i] + self.crop_size, self.re_yy[i]: self.re_yy[i] + self.crop_size] \
= crops[i]
return Image.fromarray(shuffling_img)python main_contrast.py \
--method IMPaShMoCo \
--jigsaw_stitch\
--cosine \
--dataset_name k19 \
--multiprocessing-distributed --world-size 1 --rank 0 \
--dist-url 'tcp:https://127.0.0.1:23458'
python main_linear.py \
--method PatchSMoco \
--ckpt ./save/k19_IMPaSH/ckpt_epoch_200.pth\
--aug_linear RA \
--dataset_name k19 \
--keephead head \
--multiprocessing-distributed --world-size 1 --rank 0 \
--dist-url 'tcp:https://127.0.0.1:23458'
python main_infer.py \
--method IMPaSH \
--ckpt ./save/k19_IMPaSH/ckpt_epoch_200.pth\
--ckpt_class ./save/k19_IMPaSH_linear_head_True/ckpt_epoch_40.pth\
--dataset_name k16 \
--keephead head \
--multiprocessing-distributed --world-size 1 --rank 0 \
--dist-url 'tcp:https://127.0.0.1:23452'