Multiple Instance Deep AUC Maximization with attention pooling (MIDAM-att) on Histopathology (Image) Dataset

Author: Dixian Zhu,
Edited by: Zhuoning Yuan, Tianbao Yang


In this tutorial, we will learn how to quickly train a ResNet20 model by optimizing Multiple Instance Deep AUC Maximization (MIDAM) under our novel MIDAMLoss(mode='attention') and MIDAM optimizer [Ref] method on a binary classification task on Breast Cancer Histopathology dataset. After completion of this tutorial, you should be able to use LibAUC to train your own models on your own datasets.


If you find this tutorial helpful in your work, please cite our library paper and the following papers:

   title={Provable Multi-instance Deep AUC Maximization with Stochastic Pooling},
   author={Zhu, Dixian and Wang, Bokun and Chen, Zhi and Wang, Yaxing and Sonka, Milan and Wu, Xiaodong and Yang, Tianbao},
   booktitle={International Conference on Machine Learning},

Install LibAUC

Let’s start with installing our library here. In this tutorial, we will use the lastest version for LibAUC by using pip install -U.

!pip install -U libauc

Importing LibAUC

Import required libraries to use

import torch
import matplotlib.pyplot as plt
import numpy as np
from libauc.optimizers import MIDAM
from libauc.losses import MIDAMLoss
from libauc.models import ResNet20_stoc_att
from libauc.utils import set_all_seeds, collate_fn, MIL_sampling, MIL_evaluate_auc
from libauc.sampler import DualSampler
from libauc.datasets import BreastCancer, CustomDataset


These functions limit the number of sources of randomness behaviors, such as model intialization, data shuffling, etcs. However, completely reproducible results are not guaranteed across PyTorch releases [Ref].

def set_all_seeds(SEED):
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False

Introduction for Loss and Optimizer

In this section, we will introduce pAUC optimization algorithm and how to utilize MIDAMLoss function and MIDAM optimizer.


The hyper-parameters: batch size (bag-level), instance batch size (instance-level), postive sampling rate, learning rate, weight decay and margin for AUC loss.

# HyperParameters
SEED = 123
batch_size = 8
instance_batch_size = 128
sampling_rate = 0.5
lr = 5e-2
weight_decay = 5e-4
margin = 0.1
momentum = 0.1
gamma = 0.9

Load Data, initialize model and loss

In this step, we will use the Breast Cancer as benchmark dataset [Ref]. Import data to dataloader. We extend the traditional ResNet20 with an additional attention module: ResNet20_stoc_att. The data are arranged with the shape: (num_bag, num_instance_in_bag, C, H, W)

(train_data, train_labels), (test_data, test_labels) = BreastCancer(MIL_flag=True)
traindSet = CustomDataset(train_data, train_labels, return_index=True)
testSet = CustomDataset(test_data, test_labels, return_index=True)

sampler = DualSampler(dataset=traindSet, batch_size=batch_size, shuffle=True, sampling_rate=sampling_rate)
trainloader =, sampler=sampler, batch_size=batch_size, shuffle=False, collate_fn=None)
testloader =, batch_size=batch_size, shuffle=False, collate_fn=None)

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = ResNet20_stoc_att(num_classes=1).to(device)
Loss = MIDAMLoss(mode='attention',data_len=len(traindSet), gamma=gamma, margin=margin)
optimizer = MIDAM(model.parameters(), loss_fn=Loss, lr=lr, weight_decay=weight_decay, momentum=momentum)

The data shapes for training data/label and testing data/label:

 Downloading to ./data/Breast_Cancer/breast.npz
100%|██████████| 239469738/239469738 [00:03<00:00, 72018432.08it/s]
(52, 672, 3, 32, 32)
(52, 1)
(6, 672, 3, 32, 32)
(6, 1)


total_epochs = 100
decay_epoch = [50, 75]
train_auc = np.zeros(total_epochs)
test_auc = np.zeros(total_epochs)
for epoch in range(total_epochs):
    if epoch in decay_epoch:
    for idx, data in enumerate(trainloader):
        y_pred = []
        sd = []
        train_data_bags, train_labels, ids = data
        for i in range(len(ids)):
            tmp_pred, tmp_sd = MIL_sampling(bag_X=train_data_bags[i], model=model, instance_batch_size=instance_batch_size, mode='att')
        y_pred =, dim=0)
        sd =, dim=0)
        ids = torch.from_numpy(np.array(ids))
        train_labels = torch.from_numpy(np.array(train_labels))
        loss = Loss(y_pred=(y_pred,sd), y_true=train_labels, index=ids)

    with torch.no_grad():
        single_tr_auc = MIL_evaluate_auc(trainloader, model, mode='att')
        single_te_auc = MIL_evaluate_auc(testloader, model, mode='att')
    train_auc[epoch] = single_tr_auc
    test_auc[epoch] = single_te_auc

    print ('Epoch=%s, BatchID=%s, Tr_AUC=%.4f, Test_AUC=%.4f, lr=%.4f'%(epoch, idx, single_tr_auc, single_te_auc,
Epoch=0, BatchID=6, Tr_AUC=0.7258, Test_AUC=0.8750, lr=0.0500
Epoch=1, BatchID=6, Tr_AUC=0.6760, Test_AUC=0.8750, lr=0.0500
Epoch=2, BatchID=6, Tr_AUC=0.7321, Test_AUC=1.0000, lr=0.0500
Epoch=3, BatchID=6, Tr_AUC=0.7054, Test_AUC=0.8750, lr=0.0500
Epoch=4, BatchID=6, Tr_AUC=0.6480, Test_AUC=0.8750, lr=0.0500
Epoch=5, BatchID=6, Tr_AUC=0.7194, Test_AUC=1.0000, lr=0.0500
Epoch=6, BatchID=6, Tr_AUC=0.6837, Test_AUC=1.0000, lr=0.0500
Epoch=7, BatchID=6, Tr_AUC=0.6862, Test_AUC=1.0000, lr=0.0500
Epoch=8, BatchID=6, Tr_AUC=0.7462, Test_AUC=1.0000, lr=0.0500
Epoch=9, BatchID=6, Tr_AUC=0.7015, Test_AUC=1.0000, lr=0.0500
Epoch=10, BatchID=6, Tr_AUC=0.7360, Test_AUC=1.0000, lr=0.0500
Epoch=11, BatchID=6, Tr_AUC=0.7679, Test_AUC=1.0000, lr=0.0500
Epoch=12, BatchID=6, Tr_AUC=0.7717, Test_AUC=1.0000, lr=0.0500
Epoch=13, BatchID=6, Tr_AUC=0.7538, Test_AUC=1.0000, lr=0.0500
Epoch=14, BatchID=6, Tr_AUC=0.7449, Test_AUC=1.0000, lr=0.0500
Epoch=15, BatchID=6, Tr_AUC=0.7168, Test_AUC=1.0000, lr=0.0500
Epoch=16, BatchID=6, Tr_AUC=0.6505, Test_AUC=1.0000, lr=0.0500
Epoch=17, BatchID=6, Tr_AUC=0.7653, Test_AUC=1.0000, lr=0.0500
Epoch=18, BatchID=6, Tr_AUC=0.7921, Test_AUC=1.0000, lr=0.0500
Epoch=19, BatchID=6, Tr_AUC=0.7819, Test_AUC=1.0000, lr=0.0500
Epoch=20, BatchID=6, Tr_AUC=0.7411, Test_AUC=1.0000, lr=0.0500
Epoch=21, BatchID=6, Tr_AUC=0.7870, Test_AUC=1.0000, lr=0.0500
Epoch=22, BatchID=6, Tr_AUC=0.7385, Test_AUC=1.0000, lr=0.0500
Epoch=23, BatchID=6, Tr_AUC=0.7436, Test_AUC=1.0000, lr=0.0500
Epoch=24, BatchID=6, Tr_AUC=0.8202, Test_AUC=1.0000, lr=0.0500
Epoch=25, BatchID=6, Tr_AUC=0.8061, Test_AUC=1.0000, lr=0.0500
Epoch=26, BatchID=6, Tr_AUC=0.8036, Test_AUC=1.0000, lr=0.0500
Epoch=27, BatchID=6, Tr_AUC=0.8010, Test_AUC=1.0000, lr=0.0500
Epoch=28, BatchID=6, Tr_AUC=0.8253, Test_AUC=1.0000, lr=0.0500
Epoch=29, BatchID=6, Tr_AUC=0.8010, Test_AUC=1.0000, lr=0.0500
Epoch=30, BatchID=6, Tr_AUC=0.7436, Test_AUC=1.0000, lr=0.0500
Epoch=31, BatchID=6, Tr_AUC=0.8112, Test_AUC=1.0000, lr=0.0500
Epoch=32, BatchID=6, Tr_AUC=0.8163, Test_AUC=1.0000, lr=0.0500
Epoch=33, BatchID=6, Tr_AUC=0.8099, Test_AUC=1.0000, lr=0.0500
Epoch=34, BatchID=6, Tr_AUC=0.8342, Test_AUC=1.0000, lr=0.0500
Epoch=35, BatchID=6, Tr_AUC=0.8087, Test_AUC=1.0000, lr=0.0500
Epoch=36, BatchID=6, Tr_AUC=0.8087, Test_AUC=1.0000, lr=0.0500
Epoch=37, BatchID=6, Tr_AUC=0.8176, Test_AUC=1.0000, lr=0.0500
Epoch=38, BatchID=6, Tr_AUC=0.8227, Test_AUC=1.0000, lr=0.0500
Epoch=39, BatchID=6, Tr_AUC=0.8495, Test_AUC=1.0000, lr=0.0500
Epoch=40, BatchID=6, Tr_AUC=0.8508, Test_AUC=1.0000, lr=0.0500
Epoch=41, BatchID=6, Tr_AUC=0.8431, Test_AUC=1.0000, lr=0.0500
Epoch=42, BatchID=6, Tr_AUC=0.8036, Test_AUC=1.0000, lr=0.0500
Epoch=43, BatchID=6, Tr_AUC=0.8546, Test_AUC=1.0000, lr=0.0500
Epoch=44, BatchID=6, Tr_AUC=0.8584, Test_AUC=1.0000, lr=0.0500
Epoch=45, BatchID=6, Tr_AUC=0.8355, Test_AUC=1.0000, lr=0.0500
Epoch=46, BatchID=6, Tr_AUC=0.8291, Test_AUC=1.0000, lr=0.0500
Epoch=47, BatchID=6, Tr_AUC=0.8482, Test_AUC=1.0000, lr=0.0500
Epoch=48, BatchID=6, Tr_AUC=0.8444, Test_AUC=1.0000, lr=0.0500
Epoch=49, BatchID=6, Tr_AUC=0.8508, Test_AUC=1.0000, lr=0.0500
Reducing learning rate to 0.00500 @ T=350!
Updating regularizer @ T=350!
Epoch=50, BatchID=6, Tr_AUC=0.8495, Test_AUC=1.0000, lr=0.0050
Epoch=51, BatchID=6, Tr_AUC=0.8304, Test_AUC=1.0000, lr=0.0050
Epoch=52, BatchID=6, Tr_AUC=0.8635, Test_AUC=1.0000, lr=0.0050
Epoch=53, BatchID=6, Tr_AUC=0.8776, Test_AUC=1.0000, lr=0.0050
Epoch=54, BatchID=6, Tr_AUC=0.8418, Test_AUC=1.0000, lr=0.0050
Epoch=55, BatchID=6, Tr_AUC=0.8240, Test_AUC=1.0000, lr=0.0050
Epoch=56, BatchID=6, Tr_AUC=0.8622, Test_AUC=1.0000, lr=0.0050
Epoch=57, BatchID=6, Tr_AUC=0.8635, Test_AUC=1.0000, lr=0.0050
Epoch=58, BatchID=6, Tr_AUC=0.8559, Test_AUC=1.0000, lr=0.0050
Epoch=59, BatchID=6, Tr_AUC=0.8508, Test_AUC=1.0000, lr=0.0050
Epoch=60, BatchID=6, Tr_AUC=0.8291, Test_AUC=1.0000, lr=0.0050
Epoch=61, BatchID=6, Tr_AUC=0.8240, Test_AUC=1.0000, lr=0.0050
Epoch=62, BatchID=6, Tr_AUC=0.8661, Test_AUC=1.0000, lr=0.0050
Epoch=63, BatchID=6, Tr_AUC=0.8559, Test_AUC=1.0000, lr=0.0050
Epoch=64, BatchID=6, Tr_AUC=0.8304, Test_AUC=1.0000, lr=0.0050
Epoch=65, BatchID=6, Tr_AUC=0.8444, Test_AUC=1.0000, lr=0.0050
Epoch=66, BatchID=6, Tr_AUC=0.8482, Test_AUC=1.0000, lr=0.0050
Epoch=67, BatchID=6, Tr_AUC=0.8801, Test_AUC=1.0000, lr=0.0050
Epoch=68, BatchID=6, Tr_AUC=0.8355, Test_AUC=1.0000, lr=0.0050
Epoch=69, BatchID=6, Tr_AUC=0.8699, Test_AUC=1.0000, lr=0.0050
Epoch=70, BatchID=6, Tr_AUC=0.8367, Test_AUC=1.0000, lr=0.0050
Epoch=71, BatchID=6, Tr_AUC=0.8482, Test_AUC=1.0000, lr=0.0050
Epoch=72, BatchID=6, Tr_AUC=0.8597, Test_AUC=1.0000, lr=0.0050
Epoch=73, BatchID=6, Tr_AUC=0.8673, Test_AUC=1.0000, lr=0.0050
Epoch=74, BatchID=6, Tr_AUC=0.8673, Test_AUC=1.0000, lr=0.0050
Reducing learning rate to 0.00050 @ T=525!
Updating regularizer @ T=525!
Epoch=75, BatchID=6, Tr_AUC=0.8367, Test_AUC=1.0000, lr=0.0005
Epoch=76, BatchID=6, Tr_AUC=0.9005, Test_AUC=1.0000, lr=0.0005
Epoch=77, BatchID=6, Tr_AUC=0.8686, Test_AUC=1.0000, lr=0.0005
Epoch=78, BatchID=6, Tr_AUC=0.8661, Test_AUC=1.0000, lr=0.0005
Epoch=79, BatchID=6, Tr_AUC=0.8750, Test_AUC=1.0000, lr=0.0005
Epoch=80, BatchID=6, Tr_AUC=0.8508, Test_AUC=1.0000, lr=0.0005
Epoch=81, BatchID=6, Tr_AUC=0.8597, Test_AUC=1.0000, lr=0.0005
Epoch=82, BatchID=6, Tr_AUC=0.8520, Test_AUC=1.0000, lr=0.0005
Epoch=83, BatchID=6, Tr_AUC=0.8214, Test_AUC=1.0000, lr=0.0005
Epoch=84, BatchID=6, Tr_AUC=0.8061, Test_AUC=1.0000, lr=0.0005
Epoch=85, BatchID=6, Tr_AUC=0.8737, Test_AUC=1.0000, lr=0.0005
Epoch=86, BatchID=6, Tr_AUC=0.8686, Test_AUC=1.0000, lr=0.0005
Epoch=87, BatchID=6, Tr_AUC=0.8469, Test_AUC=1.0000, lr=0.0005
Epoch=88, BatchID=6, Tr_AUC=0.8635, Test_AUC=1.0000, lr=0.0005
Epoch=89, BatchID=6, Tr_AUC=0.8686, Test_AUC=1.0000, lr=0.0005
Epoch=90, BatchID=6, Tr_AUC=0.8431, Test_AUC=1.0000, lr=0.0005
Epoch=91, BatchID=6, Tr_AUC=0.9031, Test_AUC=1.0000, lr=0.0005
Epoch=92, BatchID=6, Tr_AUC=0.8673, Test_AUC=1.0000, lr=0.0005
Epoch=93, BatchID=6, Tr_AUC=0.8495, Test_AUC=1.0000, lr=0.0005
Epoch=94, BatchID=6, Tr_AUC=0.8712, Test_AUC=1.0000, lr=0.0005
Epoch=95, BatchID=6, Tr_AUC=0.8469, Test_AUC=1.0000, lr=0.0005
Epoch=96, BatchID=6, Tr_AUC=0.8571, Test_AUC=1.0000, lr=0.0005
Epoch=97, BatchID=6, Tr_AUC=0.8329, Test_AUC=1.0000, lr=0.0005
Epoch=98, BatchID=6, Tr_AUC=0.8750, Test_AUC=1.0000, lr=0.0005
Epoch=99, BatchID=6, Tr_AUC=0.8648, Test_AUC=1.0000, lr=0.0005


plt.rcParams["figure.figsize"] = (9,5)

plt.plot(x, train_auc, linestyle='--', label='train', linewidth=3)
plt.plot(x, test_auc, label='test', linewidth=3)
plt.title('Breast Cancer',fontsize=25)

Ablation Study on Attention Weights

maxV = -1e10
maxbag = 0
for idx, data in enumerate(trainloader):
    y_pred = []
    sd = []
    train_data_bags, train_labels, ids = data
    for i in range(len(ids)):
        y_pred_bag, weights_bag = model(train_data_bags[i].float().cuda())
        value = torch.sum(y_pred_bag*weights_bag)/torch.sum(weights_bag)
        if value > maxV:
            maxV = value
            maxbag = train_data_bags[i]
            maxlabel = train_labels[i]
tensor(-1.9086, device='cuda:0', grad_fn=<DivBackward0>)
tensor([1.], dtype=torch.float64)
from mpl_toolkits.axes_grid1 import make_axes_locatable
tmp = maxbag
tmp = tmp.numpy()
tmp = np.transpose(tmp,[0,2,3,1])
img = []
for i in range(24): # collate patches back to the original image arrangement
    tmpimg = []
    for j in range(28):
    tmpimg = np.concatenate(tmpimg, axis=1)
img = np.concatenate(img, axis=0)

y_pred_bag, weights_bag = model(maxbag.float().cuda())
weights = weights_bag.detach().cpu().numpy()
weights = np.reshape(weights, [24,28])

preds = y_pred_bag.detach().cpu().numpy()
preds = np.reshape(preds, [24,28])

fig, subfigs = plt.subplots(figsize=(15, 6), ncols=3)
imgax = subfigs[0]
predax = subfigs[1]
attax = subfigs[2]
imgfig = imgax.imshow(img, interpolation='nearest')
imgax.set_title('medical image')
predfig = predax.imshow(preds, interpolation=None, norm=None)
predax.set_title('prediction scores')
attfig = attax.imshow(weights, interpolation=None, norm=None)
attax.set_title('attention weights')
divider = make_axes_locatable(imgax)
cax = divider.new_vertical(size='5%', pad=0.1, pack_start = True)

plt.colorbar(imgfig, cax=cax, shrink=0.0, orientation = 'horizontal', pad=0.06)
plt.colorbar(predfig, ax=predax, shrink=0.4, orientation = 'horizontal', pad=0.06)
plt.colorbar(attfig, ax=attax, shrink=0.4, orientation = 'horizontal', pad=0.06)