A generalized framework for medical image classification and recognition

Abedini, Mani; Codella, Noel; Connell, Jonathan H.; Garnavi, Rahil; Merler, Michele; Pankanti, Sharath; Smith, J. R.; Syeda-Mahmood, Tanveer

doi:10.1147/jrd.2015.2390017

Cited by 28 publications

(23 citation statements)

References 29 publications

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“…This type of learning shows that to make the progress how the biological organisms are adapted [8]. For checking the accuracy of the solution idea of fitness is used.…”

Section: Evolutionary Machine Learningmentioning

confidence: 99%

Machine Learning Based Behavioral Modification

Cahyadi,

Razak,

Abdillah

et al. 2019

IJEAT

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To build up a particular profile about a person, the study of examining the comportment is known as Behavior analysis. Initially the Behavior analysis is used in psychology and for suggesting and developing different types the application content for user then it developed in information technology. To make the applications for user's personal needs it becoming a new trends with the use of artificial intelligence (AI). in many applications like innovation to do everything from anticipating buy practices to altering a home's indoor regulator to the inhabitant's optimal temperature for a specific time of day use machine learning and artificial intelligence technology. The technique that is use to advance the rule proficiency that rely upon the past experience is known as machine learning. By utilizing the insights hypothesis it makes the numerical model, and its real work is to infer from the models gave. To take the information clearly from the data the methodology utilizes computational techniques.

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“…This type of learning shows that to make the progress how the biological organisms are adapted [8]. For checking the accuracy of the solution idea of fitness is used.…”

Section: Evolutionary Machine Learningmentioning

confidence: 99%

Machine Learning Based Behavioral Modification

Cahyadi,

Razak,

Abdillah

et al. 2019

IJEAT

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“…The system is the basis for multiple efforts for medical image analysis including dermatology image classification and medical image modality classification [22]. The system has been applied to large media company data sets, including news archives, television/movie programs, and b-rolls.…”

Section: Applicationsmentioning

confidence: 99%

Massive-scale learning of image and video semantic concepts

Smith¹,

Cao²,

Codella³

et al. 2015

IBM J. Res. & Dev.

Self Cite

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Rapid growth in the capture and generation of images and videos is driving the need for more efficient and effective systems for analyzing, searching, and retrieving this data. Specific challenges include supporting automatic content indexing at a large scale and accurately extracting a sufficiently large number of relevant semantic concepts to enable effective search. In this paper, we describe the development of a system for massive-scale visual semantic concept extraction and learning for images and video. The system models the visual semantic space using a hierarchical faceted classification scheme across objects, scenes, people, activities, and events and utilizes a novel machine learning approach that creates ensemble classifiers from automatically extracted visual features. The ensemble learning and extraction processes are easily parallelizable for distributed processing using Hadoop A and IBM InfoSphere A Streams, which enable efficient processing of large data sets. We report on various applications and quantitative and qualitative results for different image and video data sets.

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“…using supervised machine learning techniques [3]. While many different types of medical images are collected [4,5], the source of the images are not always identified appropriately [6,7]. Automatic identification of the imaging modality is an initial fundamental requirement as semantics and content of an image can vary greatly depending on the modality.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Deep Transfer Feature Learning for Medical Image Classification

Ahn

Kumar

Feng

et al. 2019

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

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The accuracy and robustness of image classification with supervised deep learning are dependent on the availability of large-scale, annotated training data. However, there is a paucity of annotated data available due to the complexity of manual annotation. To overcome this problem, a popular approach is to use transferable knowledge across different domains by: 1) using a generic feature extractor that has been pre-trained on large-scale general images (i.e., transferlearned) but which not suited to capture characteristics from medical images; or 2) fine-tuning generic knowledge with a relatively smaller number of annotated images. Our aim is to reduce the reliance on annotated training data by using a new hierarchical unsupervised feature extractor with a convolutional auto-encoder placed atop of a pre-trained convolutional neural network. Our approach constrains the rich and generic image features from the pre-trained domain to a sophisticated representation of the local image characteristics from the unannotated medical image domain. Our approach has a higher classification accuracy than transfer-learned approaches and is competitive with state-ofthe-art supervised fine-tuned methods.

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A generalized framework for medical image classification and recognition

Cited by 28 publications

References 29 publications

Machine Learning Based Behavioral Modification

Machine Learning Based Behavioral Modification

Massive-scale learning of image and video semantic concepts

Unsupervised Deep Transfer Feature Learning for Medical Image Classification

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