@inproceedings{Tomczak2018,
title = {Histopathological classification of precursor lesions of esophageal adenocarcinoma: A Deep Multiple Instance Learning Approach},
author = {J. M. Tomczak, M. Ilse, M. Welling, M. Jansen, H.G. Coleman, M. Lucas, K. de Laat, M. de Bruin, H. Marquering, M. J. van der Wel, O. J. de Boer, C. D. Savci-Heijink, S. L. Meijer},
url = {https://openreview.net/pdf?id=HyNf-UcsM},
year = {2018},
date = {2018-04-10},
booktitle = {Medical Imaging with Deep Learning. MIDL Amsterdam},
journal = {Medical Imaging with Deep Learning. MIDL Amsterdam},
abstract = {In this paper, we hypothesize that morphological properties of nuclei are crucial for classifying dysplastic changes. Therefore, we propose to represent a whole histopathology slide as a collection of smaller images containing patches of nuclei and adjacent tissue. For this purpose, we use a deep multiple instance learning approach. Within this framework we first embed patches in a low-dimensional space using convolutional and fully-connected layers. Next, we combine the low-dimensional embeddings using a multiple instance learning pooling operator and eventually we use fully-connected layers to provide a classification. We evaluate our approach on esophagus cancer histopathology dataset.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Ilse2018,
title = {Attention-based deep multiple instance learning},
author = {M. Ilse, J. M. Tomczak, M. Welling},
url = {https://arxiv.org/abs/1802.04712},
year = {2018},
date = {2018-02-13},
booktitle = {International Conference on Machine Learning. ICML},
abstract = {Multiple instance learning (MIL) is a variation of supervised learning where a single class label is assigned to a bag of instances. In this paper, we state the MIL problem as learning the Bernoulli distribution of the bag label where the bag label probability is fully parameterized by neural networks. Furthermore, we propose a neural network-based permutation-invariant aggregation operator that corresponds to the attention mechanism. Notably, an application of the proposed attention-based operator provides insight into the contribution of each instance to the bag label. We show empirically that our approach achieves comparable performance to the best MIL methods on benchmark MIL datasets and it outperforms other methods on a MNIST-based MIL dataset and two real-life histopathology datasets without sacrificing interpretability.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Tomczak2017,
title = {Deep Learning with Permutation-invariant Operator for Multiple-instance Histopathology Classification},
author = {J. M. Tomczak, M. Ilse, M. Welling},
url = {https://arxiv.org/abs/1712.00310},
year = {2017},
date = {2017-12-01},
booktitle = {Medical Imaging meets NIPS Workshop},
abstract = {The computer-aided analysis of medical scans is a longstanding goal in the medical imaging field. Currently, deep learning has became a dominant methodology for supporting pathologists and radiologist. Deep learning algorithms have been successfully applied to digital pathology and radiology, nevertheless, there are still practical issues that prevent these tools to be widely used in practice. The main obstacles are low number of available cases and large size of images (a.k.a. the small n, large p problem in machine learning), and a very limited access to annotation at a pixel level that can lead to severe overfitting and large computational requirements. We propose to handle these issues by introducing a framework that processes a medical image as a collection of small patches using a single, shared neural network. The final diagnosis is provided by combining scores of individual patches using a permutation-invariant operator (combination). In machine learning community such approach is called a multi-instance learning (MIL).},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}