Computer-Aided Prostate Cancer Diagnosis From Digitized Histopathology: A Review on Texture-Based Systems

Mosquera-Lopez, Clara; Agaian, Sos S.; Vélez-Hoyos, Alejandro; Thompson, Ian M.

doi:10.1109/rbme.2014.2340401

Cited by 99 publications

(58 citation statements)

References 107 publications

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“…Th ese systems ind ex and retrieve image s based on aut omatically lear ned simil arity metrics (45). In medical uses of CBIR, doctors use a medical image (e.g., tissue from a biopsy, or an x-ray) as a query for retrieving similar images from previously diagnosed patients (1, 34,35,42). Because simil ar patients give doctors points of comparison, CBIR has been used to improve medical decision making by filling knowledge gaps, aiding consis tency, and refreshing knowledge of rare cases [ 23}.…”

Section: Introductionmentioning

confidence: 99%

Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making

Cai

Reif

Hegde

et al. 2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

284

171

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Machin e lear nin g (ML) is incr easingly being use d in image retrieval systems for medical decision making. On e app lication of ML is to retrieve visually similar medical images from pas t patients (e.g. tissue from biops ies) to reference whe n making a medical decision with a new pat ient. Howeve r, no algorithm can perfectly captu re an expert ' s ideal notion of similarity for every case: an image th at is algorithmi cally determin ed to be similar may not be medically relevant to a doctor' s specific diagnostic needs. In this pape r, we identified the needs of patho logists whe n searchin g for similar images retrieved usin g a deep lear nin g algorithm , and develope d tools that empower use rs to cope with the search algorithm on-the -fly, communi cating what types of similarity are most import ant at different moment s in time. In two evaluations with path ologists, we found th at th ese refinement tools increased the diagnos tic utility of images found and increased user trus t in the algorithm. Th e tools we re preferred over a traditi onal interface, without a loss in diagnostic accuracy. We also observe d that users adopted new str ategies whe n using refinement tools, re-purpos ing th em to test and understand the underlying algorithm and to disambiguate ML errors from their own errors. Taken togethe r, these findings inform futur e hum an-ML collabo rative systems for expe rt decision-m aking. CCS CONCEPTS• Human-centered computing --> Human computer interaction (HCI); KEYWORDS Human -AI int eraction ; machin e learnin g; clinical healthPermission to mak e digital or har d copies of part or all of this work for personal or classroom use is grant ed without fee provi ded that copies are not made or distributed for profit or commercial advanta ge and that copies bear this notice an d the full citation on the first page. Figure 1: Medical images contain a wide range of clinical features , such as cellular (1) and glandular morphology (2), interaction between components (3), processing artifacts (4), and many more. It can be difficult for a similar -image search algorithm to perfectly capture an expert's notion of similarity ,

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Section: Introductionmentioning

confidence: 99%

Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making

Cai

Reif

Hegde

et al. 2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

284

171

View full text Add to dashboard Cite

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“…Evaluation of microscopic histopathology slides by experienced pathologists is currently the standard procedure for establishing a diagnosis and identifying the subtypes of different cancers. Visual-only assessment of wellestablished histopathology patterns is typically slow, and is shown to be inaccurate and irreproducible in certain diagnosis cases of tumor subtypes and stages [58]. Several recent studies attempted to employ machine learning approaches for determining subtypes of malignancies [19,87].…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images

Khosravi

Kazemi

Imieliński

et al. 2017

Preprint

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Pathological evaluation of tumor tissue is pivotal for diagnosis in cancer patients and automated image analysis approaches have great potential to increase precision of diagnosis and help reduce human error.In this study, we utilize various computational methods based on convolutional neural networks (CNN) and build a stand-alone pipeline to effectively classify different histopathology images across different types of cancer. In particular, we demonstrate the utility of our pipeline to discriminate between two subtypes of lung cancer, four biomarkers of bladder cancer, and five biomarkers of breast cancer. In addition, we apply our pipeline to discriminate among four immunohistochemistry (IHC) staining scores of bladder and breast cancers.Our classification pipeline utilizes a basic architecture of CNN, Google's Inceptions within three training strategies, and an ensemble of two state-of-the-art algorithms, Inception and ResNet. These strategies include training the last layer of Google's Inceptions, training the network from scratch, and fine-tunning the parameters for our data using two pre-trained version of Google's Inception architectures, Inception-V1 and Inception-V3.We demonstrate the power of deep learning approaches for identifying cancer subtypes, and the robustness of Google's Inceptions even in presence of extensive tumor heterogeneity. Our pipeline on average achieved accuracies of 100%, 92%, 95%, and 69% for discrimination of various cancer types, subtypes, biomarkers, and scores, respectively. Our pipeline and related documentation is freely available at https://github.com/ ih-lab/CNN_Smoothie.

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“…N−grams are sequences of n− words [5]. Reference [9] provides detailed description of literature in CAD, emphasizing the role of tissue texture, also provides review on image processing tools for feature extraction, classification and validation for cancer detection and grading.…”

Section: Related Workmentioning

confidence: 99%

Object Detection in a Cluttered Scene Using SURF for Computer Assisted Histopathology

Abbas¹,

Zehra²,

Fu³

2016

Proceedings of the 2016 International Conference on Electrical, Mechanical and Industrial Engineering

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Abstract-Computer vision imitates human vision ability to perceive real world. Computer vision has proven its part from autonomous navigation on martin surface to histopathology images recognition. In practice, histopathologists examine microscopic imagery of diseased tissue and diagnose on the bases of malignancy, likewise a computer assisted diagnosis (CAD) system is devised for histopathology images recognition on the basis of visual features. This paper describes the design methodology, workflow and test results of locating an object in a cluttered scene. In this paper speeded up robust features are used for feature extraction and matching. After object detection, Random sample consensus is used for removing outliers to refine results. This CAD system provides a heuristic approach to assist histopathologists in diagnostics.

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Computer-Aided Prostate Cancer Diagnosis From Digitized Histopathology: A Review on Texture-Based Systems

Cited by 99 publications

References 107 publications

Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making

Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making

Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images

Object Detection in a Cluttered Scene Using SURF for Computer Assisted Histopathology

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