Deep learning of feature representation with multiple instance learning for medical image analysis

Xu, Yan; Tao, Mu‐Xuan; Feng, Qiwei; Zhong, Peilin; Lai, Maode; Chang, Eric

doi:10.1109/icassp.2014.6853873

Cited by 291 publications

(175 citation statements)

References 17 publications

(23 reference statements)

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“…Wu et al [6] developed deep feature learning for deformable registration of brain MR images to improve image registration by using deep features. Xu et al [7] presented the effectiveness of using deep neural networks (DNNs) for feature extraction in medical image analysis as a supervised approach. Kumar et al [8] proposed a CAD system which uses deep features extracted from an autoencoder to classify lung nodules as either malignant or benign on LIDC database.…”

Section: Methodsmentioning

confidence: 99%

Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN)

Alakwaa¹,

Nassef²,

Badr³

2017

ijacsa

183

100

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Abstract-This paper demonstrates a computer-aided diagnosis (CAD) system for lung cancer classification of CT scans with unmarked nodules, a dataset from the Kaggle Data Science Bowl, 2017. Thresholding was used as an initial segmentation approach to segment out lung tissue from the rest of the CT scan. Thresholding produced the next best lung segmentation. The initial approach was to directly feed the segmented CT scans into 3D CNNs for classification, but this proved to be inadequate. Instead, a modified U-Net trained on LUNA16 data (CT scans with labeled nodules) was used to first detect nodule candidates in the Kaggle CT scans. The U-Net nodule detection produced many false positives, so regions of CTs with segmented lungs where the most likely nodule candidates were located as determined by the U-Net output were fed into 3D Convolutional Neural Networks (CNNs) to ultimately classify the CT scan as positive or negative for lung cancer. The 3D CNNs produced a test set Accuracy of 86.6%. The performance of our CAD system outperforms the current CAD systems in literature which have several training and testing phases that each requires a lot of labeled data, while our CAD system has only three major phases (segmentation, nodule candidate detection, and malignancy classification), allowing more efficient training and detection and more generalizability to other cancers.

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Section: Methodsmentioning

confidence: 99%

Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN)

Alakwaa¹,

Nassef²,

Badr³

2017

ijacsa

183

100

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“…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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“…(3) describes that all the edges of medical image objects are the lies between the (G) = √G(x i , y i ) 2 , since the G is targeted connected set of lines to be formed around the (x i , y i ) spatial locations. G x and G y Angles could also be used to measure the directions of objects which are involved in expected expansion over multiple regions eq.…”

Section: Canny Edge Detectionmentioning

confidence: 99%

“…A system [2] using Convolutional neural network based machine learning technique was proposed for thyroid disease classification. The DICOM images need significant pre-processing techniques for every individual class of disease such as welldifferentiated, poorly differentiated and others.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Initiative For Thyroid Cancer Diagnosis: Decision Support System For Anaplast Thyroid Cancer

Chandio

Rehman

Sehito

et al. 2017

SJCMS

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Due to the high level exposure of biomedical image analysis, Medical image mining has become one of the well-established research area(s) of machine learning. AI (Artificial Intelligence) techniques have been vastly used to solve the complex classification problems of thyroid cancer. Since the persistence of copycat chromatin properties and unavailability of nuclei measurement techniques, it is really problem for doctors to determine the initial phases of nuclei enlargement and to assess the early changes of chromatin distribution. For example involvement of multiple transparent overlapping of nuclei may become the cause of confusion to infer the growth pattern of nuclei variations. Un-decidable nuclei eccentric properties may become one of the leading causes for misdiagnosis in Anaplast cancers. In-order to mitigate all above stated problems this paper proposes a novel methodology so called "Decision Support System for Anaplast Thyroid Cancer" and it proposes a medical data preparation algorithm AD (Analpast_Cancers) which helps to select the appropriate features of Anaplast cancers such as (1) enlargement of nuclei, (2) persistence of irregularity in nuclei and existence of hyper chromatin. Proposed methodology comprises over four major layers, the first layer deals with the noise reduction, detection of nuclei edges and object clusters. The Second layer selects the features of object of interest such as nuclei enlargement, irregularity and hyper chromatin. The Third layer constructs the decision model to extract the hidden patterns of disease associated variables and the final layer evaluates the performance evaluation by using confusion matrix, precision and recall measures. The overall classification accuracy is measured about 97.2% with 10-k fold cross validation.

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Deep learning of feature representation with multiple instance learning for medical image analysis

Cited by 291 publications

References 17 publications

Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN)

Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN)

Multiple Instance Cancer Detection by Boosting Regularised Trees

Initiative For Thyroid Cancer Diagnosis: Decision Support System For Anaplast Thyroid Cancer

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