1 Motivation
One of the major problems in medical diagnosis is the subjectivity of the specialist’s decisions. More specifically, in the fields of medical imaging interpretation, the experience of the specialist can greatly determine the outcome of the final diagnosis. Manual inspection of medical images can in some cases be very tedious, timeconsuming and subject to errors on part of the interpreter. This has led to a growth of computer vision algorithms to support medical diagnostics. The wide success of deep learning in computer vision has increased the accuracy of the predictions, renovating the interest in computerassisted medical diagnosis.
Nevertheless, deep convolutional neural networks (CNNs) are defined by a huge amount of trainable parameters that require very large amounts of labeled data for training. This may be a heavy handicap in the medical imaging field, where the annotation costs of highly qualified professionals make very challenging the generation of large labeled datasets. Active Learning (AL) is an established approach to reduce this labeling workload in order to select, in an iterative way, a subset of informative examples from a large collections of unlabeled images. The chosen candidates are labeled and subsequently added to the training set.
There exist multiple active learning methods that choose which samples are to be annotated by the human experts. In this work we explore the uncertainty of the pixelwise predictions as a selection criterion.
The main contributions of this work are the following:

The design and training of a framework for medical imaging semantic segmentation using Convolutional Neuronal Networks (CNN) and the CostEffective Active Learning method.

The development of information interpreters for medical imaging based on Monte Carlo Dropout for the analysis of the intrinsic network distribution.
The source code of this work is publicly available at https://marcgorriz.github.io/CEALMedicalImageSegmentation/.
2 Related work
2.1 CostEffective Active Learning (CEAL) algorithm
An active learning is an algorithm able to interactively query the human annotator (or some other information source) new labeled instances from a pool of unlabeled data. Candidates to be labeled can be chosen with several methods based on informativeness and uncertainty of the data. Opposite to common Active Learning approaches that only consider the most informative and representative samples, a CostEffective methodology Wang et al. (2016) proposes to automatically select and pseudoannotate unlabeled samples. A CEAL progressively feeds the samples from the unlabeled dataset into the CNN, and selects two kinds of samples for finetuning according to the CNN’s output. The sampling selection procedure is also known as complementary sample selection. One type of samples are a minority samples with low prediction confidence, which correspond to the most informative/uncertain samples whose prediction score is low. The selected samples are added into the labeled set after an active user (oracle) labels them. Another type of samples are the majority samples with high prediction score, because their prediction is highly confident. For these certain kind of samples, the proposed CEAL automatically assigns pseudolabels with no human cost. These two types of samples are complementary to each other for representing different confidence levels of the current model on the unlabeled dataset.
2.2 CNN’s for Image Segmentation: UNet architecture
Our active learning approach is applied over a popular convolutional neural network (CNN) in the medical domain: the UNet. The UNet Ronneberger et al. (2015) is a convolutional neural network architecture designed to solve biomedical image segmentation tasks. It was successfully used for winning the ISBI cell tracking challenge in 2015. The network combines a convolutional network architecture with a deconvolutional architecture to obtain the semantic segmentation Noh et al. (2015); Krähenbühl and Koltun (2011). The convolutional network is composed of a repetitive pattern of two x
convolutions operations, followed by a ReLU layer and a downsampling process through a
xmaxpooling operation with stride
. On the other hand, the deconvolutional architecture includes a upsampling operation of the feature map obtained during the contracting path, followed by a x deconvolution that fractions the feature map channels into . A posteriori concatenation of the resulting feature map and the obtained during the contracting path is needed, followed by a x convolutions and a ReLU layer. The entire network isconvolutional layers deep, where the last layer is used to map each component feature vector related to the number of classes.
3 Proposed methodology
3.1 Image uncertainty estimation
The active learning criteria used to perform the complementary sample selection is based on the intrinsic distribution of the unlabeled data, ranking the unlabeled pool based on their influence on the model. Being , the unlabeled data and their labels respectively, we are interested in finding the posterior network distribution . In general, this posterior distribution is not tractable, therefore we need to approximate the distribution of these weights using variational inference Graves (2011). This technique allows us to learn the distribution over the network’s weights, , by minimizing the KullbackLeibler (KL) divergence between this approximating distribution and the full posterior one.
In Kendall et al. (2015) it is explored the possibility to use Monte Carlo Dropout to approximate term. The dropout works by randomly deactivating network activations following a Bernoulli
distribution with a basis probability
per layer. With all the above, the introduction of Dropout during the training prevents overfitting Gal and Ghahramani (2015), while at test time it allows introducing pixelwise sample uncertainty. Beinga image pixel, we can estimate the uncertainty of its predicted label
computing the variance of
different predictions on the same pixel by the effect of Dropout through the network weights. The precision of pixelwise uncertainty maps will depend on the Dropout steps and the Dropout probability . High means high variation of network weights, making difficult a consistent result with a finite number of step predictions. As was shown in Kendall et al. (2015) an optimal value is and a maximum precision would be obtained when .In order to integrate the method to the CEAL complementary sample selection, the pixelwise uncertainty must be transformed into a numerical score to estimate the prediction confidence. We propose summing up all the pixel values from the uncertainty map, obtaining this way higher scores for those most doubtful segmentations.
However, a simple pixelwise addition does not cope with the variability of predictions across the contours. The further from the contour, the greater should be the contribution to the overall uncertainty of the prediction. For this reason, we introduce an additional weighting step based on the distance map over the predicted segmentation, which corresponds to computing the euclidean distance of each pixel to the closest pixel of the contour Breu et al. (1995). This distance map is multiplied with the uncertainty map, so that the values of the further pixels from the predicted contour are boosted. Intuitively, this processing maker thicker the thickest contours in the uncertainty maps, and thinner the thinnest ones.
3.2 Complementary sample selection
Once the uncertainty score is defined, we can visualize its relation with the accuracy of the predictions. Figure 2 (left) shows the correlation between both, using as predictor the best model found in the next Section 4. The plot allows distinguishing four types of samples:

Undetected melanomas: although they have low uncertainty, they are also the most informative candidates to be manually annotated because they correspond to missing detections but with high certainty.

Highly uncertain samples: secondary candidates to be annotated by the oracle.

Certain samples: the most common ones, best candidates to be selected as a pseudolabels.

Uncertain and wrong predictions: the most undesirable case whose population aims at be reduce by iterating the active learning algorithm.
Notice that in the real case scenario, the accuracy of the predictions cannot be computed because the ground truth of the analyzed samples is not available. In a real case only the uncertainty values can be estimated. A histogram of these uncertainty values is depitced in 2 (right), which corresponds to a projection of 2 (left) over the uncertainty axis. In this case, samples between the third and fourth regions become shuffled and cannot be discriminated. With all the above, the complementary data selection method will plan a strategy to select sequentially the correct proportion of samples for each region in each iteration, to improve gradually the network performance, without falling into overfitting.
4 Results and Future work
All the tests were done with the ISIC 2017 Challenge dataset for Skin Lesion Analysis towards melanoma detection Gutman et al. (2016). Each image in this dataset has a pixelwise segmentation of the melanomas. In order to simulate an active learning scenario, our experiments only used a small portion of available annotations. The amount of segments used for training the CNN would increase based on the decisions of the proposed active learning approach, as if they were generated online by human annotators.
The dataset contains RGB dermoscopy images manually annotated by medical experts, by tracing the lesion boundaries in the form of a binary mask (International Skin Imaging Collaboration). The dataset was modified for this work, transforming the original images to gray scale and modifying their aspect ratio to fit the CNN input dimensions. Before starting the interactive learning process, the training sets are initialized based on the CostEffective Active Learning methodology. First, it is randomly selected the labeled amount from the dataset and then the other labels are deleted initializing the unlabeled set .
The CNN was trained by applying data augmentation over the training samples as in Krizhevsky et al. (2012), to improve the generalization of the model.
The selection of samples in each iteration of the active learning loop was made according to the heuristic parameters summarized in the tables of Figure
3. The iterative algorithm starts by training a CNN with 600 samples and iteratively selects among a pool of 1,000 images which ones are to be labeled and added to the training set. In each iteration, the proposed algorithm samples 10 images where among those where no melanoma was detected, 10 more images among those with a highest uncertainty score, and 15 more images selected randomly. Also, those predictions with a confidence score over a certain threshold was selected as pseudolabels and also included in the training set. Figure 3 also depicts the selected samples at some iterations 0, 4, 5 and 9.Initial database balancing  

Initial Set  Samples 
Labeled Set  600 
Unlabeled Set  1000 
Test Set  400 
Sample selection per iteration  
Human annotations  Samples 
Nodetections  10 
Most uncertain  10 
Random  15 
Pseudo annotations  Progressive 
The quality of the segmentations was assessed based on the Dice Coefficient as in Ronneberger et al. (2015), which is presented in Equation 1:
(1) 
where represents the predicted masks for image and its ground truth mask.
Acknowledgements
This research was supported by contract SGR1421 by the Catalan AGAUR office. The work has been developed in the framework of project TEC201675976R, funded by the Spanish Ministerio de Economia y Competitividad and the European Regional Development Fund (ERDF). The authors also thank NVIDIA for generous hardware donations.
References
 Breu et al. [1995] Heinz Breu, Joseph Gil, David Kirkpatrick, and Michael Werman. Linear time euclidean distance transform algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(5):529–533, 1995.
 Gal and Ghahramani [2015] Yarin Gal and Zoubin Ghahramani. Bayesian convolutional neural networks with bernoulli approximate variational inference. arXiv preprint arXiv:1506.02158, 2015.
 Graves [2011] Alex Graves. Practical variational inference for neural networks. In Advances in Neural Information Processing Systems, pages 2348–2356, 2011.
 Gutman et al. [2016] David Gutman, Noel CF Codella, Emre Celebi, Brian Helba, Michael Marchetti, Nabin Mishra, and Allan Halpern. Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (isbi) 2016, hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:1605.01397, 2016.
 Kendall et al. [2015] Alex Kendall, Vijay Badrinarayanan, and Roberto Cipolla. Bayesian segnet: Model uncertainty in deep convolutional encoderdecoder architectures for scene understanding. arXiv preprint arXiv:1511.02680, 2015.
 Krähenbühl and Koltun [2011] Philipp Krähenbühl and Vladlen Koltun. Efficient inference in fully connected crfs with gaussian edge potentials. In Advances in neural information processing systems, pages 109–117, 2011.
 Krizhevsky et al. [2012] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105, 2012.
 Noh et al. [2015] Hyeonwoo Noh, Seunghoon Hong, and Bohyung Han. Learning deconvolution network for semantic segmentation. In Proceedings of the IEEE International Conference on Computer Vision, pages 1520–1528, 2015.
 Ronneberger et al. [2015] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. Unet: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and ComputerAssisted Intervention, pages 234–241. Springer, 2015.
 Wang et al. [2016] Keze Wang, Dongyu Zhang, Ya Li, Ruimao Zhang, and Liang Lin. Costeffective active learning for deep image classification. IEEE Transactions on Circuits and Systems for Video Technology, 2016.
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