Digital pathology: Multiple instance learning can detect Barrett's cancer

Kandemir, Melih; Feuchtinger, Annette; Walch, Axel; Hamprecht, Fred A.

doi:10.1109/isbi.2014.6868127

Cited by 26 publications

(23 citation statements)

References 7 publications

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“…Xu et al [16] extended MILBoosting [17] to simultaneously detect and cluster multiple types of tissue region in TMA images. Kandemir et al [9] evaluated MIL formulations on diagnosis of Barrett's cancer with H&E images. Xu et al [15] used MIL to classify colon cancer histopathology images with features extracted from convolutional neural networks.…”

Section: Introductionmentioning

confidence: 99%

Multiple Instance Cancer Detection by Boosting Regularised Trees

Zhang

McKenna

2015

Lecture Notes in Computer Science

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Abstract. We propose a novel multiple instance learning algorithm for cancer detection in histopathology images. With images labelled at image-level, we first search a set of region-level prototypes by solving a submodular set cover problem. Regularised regression trees are then constructed and combined on the set of prototypes using a multiple instance boosting framework. The method compared favourably with competing methods in experiments on breast cancer tissue microarray images and optical tomographic images of colorectal polyps.

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Section: Introductionmentioning

confidence: 99%

Multiple Instance Cancer Detection by Boosting Regularised Trees

Zhang

McKenna

2015

Lecture Notes in Computer Science

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“…As input for this experiment, secondary breast cancer dataset was adopted which obtained from [11]. The breast histopathology image dataset originally presented by Center for Bio-Image Informatics in University of California, Santa Barbara (UCSB).…”

Section: Fig 1: Multi-instance Medical Image Classificationmentioning

confidence: 99%

“…Each image is a bag, and each patch within an image is an instance. A bag with positive label denotes the existence of a cancer region within the core [11]. Although MIC has been extensively studied, it was only recently applied to histopathology image analysis [12].…”

Section: Introductionmentioning

confidence: 99%

Multi-Instance Image Classification for Cancer Diagnosis using Statistical Mapping Support Vector Machine

Berahim¹,

Samsudin²,

Nathan³

2018

IJET

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This paper presents multi-instance (MI) image classification for cancer diagnosis using statistical mapping Support Vector Machine (SVM). The existing MI image classification is limited to focusing on standard multi-instance classification (MIC) assumption, but do not generalize to the whole range of MI data and do not fully utilize the power of conventional SVM. The standard MIC assumption labelled a bag of image as positive if there is at least one instance in it which is positive. Unfortunately, this assumption is not applicable if there is less information about abnormal instances provided in a bag. Therefore, the paper aims to propose conventional SVM that utilized the basic statistical mapping to form a bag vector of instances in order to classify MI images and give the benefit of the automated image diagnostic procedure. Numerical tests examine the benefit of instances' features transformation to be a vector of bag representation using mean and covariance mapping to Linear-SVM, Square-SVM and Cube-SVM. The experiments used a secondary dataset. The numerical dataset extracted breast histopathology image of 58 patients, which contains 708 features and 2002 instances. The result obtained shows that the proposed SVM can achieve 100% sensitivity after utilizing the covariance mapping with Square-SVM. It means the classification task able to detect the malignant class. In conclusion, the conventional SVM has great potential to improve medical diagnostic procedure using MI image, particularly for cancer diagnostic after adapting statistical features transformation.

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“…Features are extracted from each patch. Similarly to [7], the feature vector contains the mean and standard deviation and a normalized 12-bin frequency histogram of the pixel intensities contained in the patch. This representation is augmented with the mean local binary pattern (LBP) extracted from a 13×13 pixel grid, and with the mean of densely extracted SIFT descriptors.…”

Section: B Data Setsmentioning

confidence: 99%

Witness identification in multiple instance learning using random subspaces

Carbonneau

Granger

Gagnon

2016

2016 23rd International Conference on Pattern Recognition (ICPR)

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Abstract-Multiple instance learning (MIL) is a form of weakly-supervised learning where instances are organized in bags. A label is provided for bags, but not for instances. MIL literature typically focuses on the classification of bags seen as one object, or as a combination of their instances. In both cases, performance is generally measured using labels assigned to entire bags. In this paper, the MIL problem is formulated as a knowledge discovery task for which algorithms seek to discover the witnesses (i.e. identifying positive instances), using the weak supervision provided by bag labels. Some MIL methods are suitable for instance classification, but perform poorly in application where the witness rate is low, or when the positive class distribution is multimodal. A new method that clusters data projected in random subspaces is proposed to perform witness identification in these adverse settings. The proposed method is assessed on MIL data sets from three application domains, and compared to 7 reference MIL algorithms for the witness identification task. The proposed algorithm constantly ranks among the best methods in all experiments, while all other methods perform unevenly across data sets.

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Digital pathology: Multiple instance learning can detect Barrett's cancer

Cited by 26 publications

References 7 publications

Multiple Instance Cancer Detection by Boosting Regularised Trees

Multiple Instance Cancer Detection by Boosting Regularised Trees

Multi-Instance Image Classification for Cancer Diagnosis using Statistical Mapping Support Vector Machine

Witness identification in multiple instance learning using random subspaces

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