NDLI: Anomaly classification in digital mammography based on multiple-instance learning

Content Provider	IET Digital Library
Author	Elmoufidi, Abdelali Fahssi, Khalid El andaloussi, Said Jai Sekkaki, Abderrahim Gwenole, Quellec Lamard, Mathieu
Abstract	Cancer tissues in mammography images exhibit abnormal regions; it is of great clinical importance to label a mammography image as having cancerous regions or not, perform the corresponding image segmentation. However, the detailed annotation of the cancer region is often an ambiguous and challenging task. The authors describe a fully automatic computer-aided detection and diagnosis (CAD) system to detect and classify breast cancer as malignant or benign, by using mammography and building on the multiple-instance learning (MIL) algorithms, which has been confirmed beneficial for radiologist decision sustenance. Traditional learning methods require great effort to annotate the training data by costly manual labelling and specialised computational models to detect these annotations during the test. The proposed CAD system simultaneously performs pixel-level segmentation (suspicious versus normal tissue) and image-level classification (benign versus malignant image). The set-up of the proposed system is in order: automatically segmented regions of interest (ROIs). Then, features derived from ROIs detected such as textural features and shape features are selected and extracted from each region and combined them to classify ROIs as ‘benign’ or ‘malignant’, by implementing MIL algorithms. Experimental results demonstrate the efficiency and robustness of the proposed CAD system compared with previous work in the literature.
Starting Page	320
Ending Page	328
Page Count	9
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 3, Mar (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0536
Journal	IET Image Processing
Publisher Date	2017-11-03
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Anomaly Classification Biology And Medical Computing Biomedical Imaging/measurement CAD System Cancer Cancer Tissues Computer Vision And Image Processing Technique Digital Database For Screening Mammography Digital Mammography Feature Extraction Feature Selection Fully Automatic Computer-aided Detection And Diagnosis System Image Classification Image Recognition Image Segmentation Image-level Classification Learning in AI Mammography Mammography Image Analysis Society Database Medical Image Processing MIL Algorithm Multiple-instance Learning Patient Diagnostic Method And Instrumentation Pixel-level Segmentation Radiography And Computed Tomography Shape Feature Extraction Textural Feature Extraction X-Ray Technique X-Rays And Particle Beam
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Computer-aided mammogram diagnosis system using deep learning convolutional fully complex-valued relaxation neural network classifier

Attribute relaxation from class level to instance level for zero-shot learning

Animal classification using facial images with score-level fusion

Online signature verification using segment-level fuzzy modelling

Weakly supervised classification model for zero-shot semantic segmentation

Classification of mammogram using two-dimensional discrete orthonormal S-transform for breast cancer detection

An Ensemble Retinal Vessel Segmentation Based on Supervised Learning in Fundus Images

Convolutional encoder–decoder networks for pixel-wise ear detection and segmentation

Segmentation-based recognition system for handwritten Bangla and Devanagari words using conventional classification and transfer learning

Anomaly classification in digital mammography based on multiple-instance learning

Similar Documents

Computer-aided mammogram diagnosis system using deep learning convolutional fully complex-valued relaxation neural network classifier

Attribute relaxation from class level to instance level for zero-shot learning

Animal classification using facial images with score-level fusion

Online signature verification using segment-level fuzzy modelling

Weakly supervised classification model for zero-shot semantic segmentation

Classification of mammogram using two-dimensional discrete orthonormal S-transform for breast cancer detection

An Ensemble Retinal Vessel Segmentation Based on Supervised Learning in Fundus Images

Convolutional encoder–decoder networks for pixel-wise ear detection and segmentation

Segmentation-based recognition system for handwritten Bangla and Devanagari words using conventional classification and transfer learning

Anomaly classification in digital mammography based on multiple-instance learning