Multi-cell Detection and Classification Using a Generative Convolutional Model

Yellin, Florence; Haeffele, Benjamin D.; Roth, Sophie; Vidal, René

doi:10.1109/cvpr.2018.00933

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…In the past decade, the renaissance in deep learning has greatly promoted the development of medical image computing [30], [31], [32], [33], [34], [35], [36], [37], [38], [39]. Mass detection has achieved remarkable success by virtue of deep convolutional neural networks [8], [9], [10].…”

Section: Mammogram Mass Detectionmentioning

confidence: 99%

Act Like a Radiologist: Towards Reliable Multi-view Correspondence Reasoning for Mammogram Mass Detection

Liu¹,

Zhang

Chen

et al. 2021

Preprint

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Mammogram mass detection is crucial for diagnosing and preventing the breast cancers in clinical practice. The complementary effect of multi-view mammogram images provides valuable information about the breast anatomical prior structure and is of great significance in digital mammography interpretation. However, unlike radiologists who can utilize the natural reasoning ability to identify masses based on multiple mammographic views, how to endow the existing object detection models with the capability of multi-view reasoning is vital for decision-making in clinical diagnosis but remains the boundary to explore. In this paper, we propose an Anatomy-aware Graph convolutional Network (AGN), which is tailored for mammogram mass detection and endows existing detection methods with multi-view reasoning ability. The proposed AGN consists of three steps. Firstly, we introduce a Bipartite Graph convolutional Network (BGN) to model the intrinsic geometric and semantic relations of ipsilateral views. Secondly, considering that the visual asymmetry of bilateral views is widely adopted in clinical practice to assist the diagnosis of breast lesions, we propose an Inception Graph convolutional Network (IGN) to model the structural similarities of bilateral views. Finally, based on the constructed graphs, the multi-view information is propagated through nodes methodically, which equips the features learned from the examined view with multi-view reasoning ability. Experiments on two standard benchmarks reveal that AGN significantly exceeds the state-of-the-art performance. Visualization results show that AGN provides interpretable visual cues for clinical diagnosis.

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Section: Mammogram Mass Detectionmentioning

confidence: 99%

Act Like a Radiologist: Towards Reliable Multi-view Correspondence Reasoning for Mammogram Mass Detection

Liu¹,

Zhang

Chen

et al. 2021

Preprint

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“…In disease diagnosis, the doctors should be well-versed about WBCs in terms of its count, type, shape and composition. It is important to maintain an optimum number of WBCs in blood, especially in case of hemorrhoid [8].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Leukemia Disease Based on Enhanced Virtual Neural Network

Muthumayil¹,

Manikandan²,

Srinivasan³

et al. 2021

Computers, Materials &Amp; Continua

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White Blood Cell (WBC) cancer or leukemia is one of the serious cancers that threaten the existence of human beings. In spite of its prevalence and serious consequences, it is mostly diagnosed through manual practices. The risks of inappropriate, sub-standard and wrong or biased diagnosis are high in manual methods. So, there is a need exists for automatic diagnosis and classification method that can replace the manual process. Leukemia is mainly classified into acute and chronic types. The current research work proposed a computer-based application to classify the disease. In the feature extraction stage, we use excellent physical properties to improve the diagnostic system's accuracy, based on Enhanced Color Co-Occurrence Matrix. The study is aimed at identification and classification of chronic lymphocytic leukemia using microscopic images of WBCs based on Enhanced Virtual Neural Network (EVNN) classification. The proposed method achieved optimum accuracy in detection and classification of leukemia from WBC images. Thus, the study results establish the superiority of the proposed method in automated diagnosis of leukemia. The values achieved by the proposed method in terms of sensitivity, specificity, accuracy, and error rate were 97.8%, 89.9%, 76.6%, and 2.2%, respectively. Furthermore, the system could predict the disease in prior through images, and the probabilities of disease detection are also highly optimistic.

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