Development and Validation of an Unsupervised Feature Learning System for Leukocyte Characterization and Classification: A Multi-Hospital Study

Yan, Hong; Mao, Xuanyu; Yang, Xu; Xia, Yongquan; Wang, Chengbin; Wang, Junjun; Xia, Rui; Xu, Xuejing; Wang, Zhiqiang; Li, Zhiyang; Xie, Zhen; Li, Yan; Liu, Guoye; He, Li; Wang, Zhongyu; Wang, Zhiqiong; Li, Zhiqiang; Cai, Weidong; Shen, Han; Chang, Hang

doi:10.1007/s11263-021-01449-9

Cited by 6 publications

(4 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Following our previous work[ 10 ], we deployed an unsupervised feature learning pipeline, which was based on the stacked predictive sparse decomposition (SPSD)[ 24 , 25 ], for unsupervised discovery of underlying cellular morphometric characteristics from 15 cellular morphological features that were extracted from the diagnostic slides from the TCGA-BRCA cohort. 256 cellular morphometric biomarkers (CMB) were defined for cellular object representation.…”

Section: Methodsmentioning

confidence: 99%

“…We extended the unsupervised feature learning pipeline (SPSD)[ 24 , 25 ] to achieve efficient and effective mining of multi-modal biomarker signatures from prebuilt cellular-morphometrics, microbiome, and gene biomarkers. Given X = [x 1 ,…,x N ] ∈ R m × N as a set of patients (N) with a combination of biomarkers from different modalities ( i.e.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

iCEMIGE: Integration of CEll-morphometrics, MIcrobiome, and GEne biomarker signatures for risk stratification in breast cancers

Mao¹,

Pérez-Losada²,

Abad³

et al. 2022

WJCO

Self Cite

View full text Add to dashboard Cite

BACKGROUND The development of precision medicine is essential for personalized treatment and improved clinical outcome, whereas biomarkers are critical for the success of precision therapies. AIM To investigate whether iCEMIGE (integration of CEll-morphometrics, MIcro biome, and GEne biomarker signatures) improves risk stratification of breast cancer (BC) patients. METHODS We used our recently developed machine learning technique to identify cellular morphometric biomarkers (CMBs) from the whole histological slide images in The Cancer Genome Atlas (TCGA) breast cancer (TCGA-BRCA) cohort. Multivariate Cox regression was used to assess whether cell-morphometrics prognosis score (CMPS) and our previously reported 12-gene expression prognosis score (GEPS) and 15-microbe abundance prognosis score (MAPS) were independent prognostic factors. iCEMIGE was built upon the sparse representation learning technique. The iCEMIGE scoring model performance was measured by the area under the receiver operating characteristic curve compared to CMPS, GEPS, or MAPS alone. Nomogram models were created to predict overall survival (OS) and progress-free survival (PFS) rates at 5- and 10-year in the TCGA-BRCA cohort. RESULTS We identified 39 CMBs that were used to create a CMPS system in BCs. CMPS, GEPS, and MAPS were found to be significantly independently associated with OS. We then established an iCEMIGE scoring system for risk stratification of BC patients. The iGEMIGE score has a significant prognostic value for OS and PFS independent of clinical factors (age, stage, and estrogen and progesterone receptor status) and PAM50-based molecular subtype. Importantly, the iCEMIGE score significantly increased the power to predict OS and PFS compared to CMPS, GEPS, or MAPS alone. CONCLUSION Our study demonstrates a novel and generic artificial intelligence framework for multimodal data integration toward improving prognosis risk stratification of BC patients, which can be extended to other types of cancer.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

iCEMIGE: Integration of CEll-morphometrics, MIcrobiome, and GEne biomarker signatures for risk stratification in breast cancers

Mao¹,

Pérez-Losada²,

Abad³

et al. 2022

WJCO

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, these methods exhibit marked limitations in terms of efficiency and generalization for solving complex problems [17,18]. Therefore, several researchers have focused on investigating deep-learning-based features [6,[19][20][21][22][23][24][25][26]. Lu et al [27] extracted and fused multiscale features using a feature encoder with residual blocks to develop a WBC segmentation system.…”

Section: Related Workmentioning

confidence: 99%

Feature Extraction of White Blood Cells Using CMYK-Moment Localization and Deep Learning in Acute Myeloid Leukemia Blood Smear Microscopic Images

et al. 2022

View full text Add to dashboard Cite

Artificial intelligence has revolutionized medical diagnosis, particularly for cancers. Acute myeloid leukemia (AML) diagnosis is a tedious protocol that is prone to human and machine errors. In several instances, it is difficult to make an accurate final decision even after careful examination by an experienced pathologist. However, computer-aided diagnosis (CAD) can help reduce the errors and time associated with AML diagnosis. White Blood Cells (WBC) detection is a critical step in AML diagnosis, and deep learning is considered a state-of-the-art approach for WBC detection. However, the accuracy of WBC detection is strongly associated with the quality of the extracted features used in training the pixel-wise classification models. CAD depends on studying the different patterns of changes associated with WBC counts and features. In this study, a new hybrid feature extraction method was developed using image processing and deep learning methods. The proposed method consists of two steps: 1) a region of interest (ROI) is extracted using the CMYK-moment localization method and 2) deep learning-based features are extracted using a CNN-based feature fusion method. Several classification algorithms are used to evaluate the significance of the extracted features. The proposed feature extraction method was evaluated using an external dataset and benchmarked against other feature extraction methods. The proposed method achieved excellent performance, generalization, and stability using all the classifiers, with overall classification accuracies of 97.57% and 96.41% using the primary and secondary datasets, respectively. This method has opened a new alternative to improve the detection of WBCs, which could lead to a better diagnosis of AML.INDEX TERMS Acute myeloid leukemia (AML), white blood cell (WBC) feature extraction, deep learning, feature fusion, CNN, ROI.

show abstract

“…Moreover, whereas early data-driven works were based on machine learning or computer vision algorithms, such as random forest [11] and geodesic distance-based systems [12], in the latest years the attention has shifted towards deep learning methods [13]. This is a consequence of the high performances obtained in various heterogeneous fields such as emotion recognition [14,15], medical image analysis [16,17], and person re-identification [18,19], as well as the availability of commodity hardware in capture systems [20] that can provide different input types (e.g., depth maps).…”

Section: Introductionmentioning

confidence: 99%

3D Hand Pose and Shape Estimation from RGB Images for Keypoint-Based Hand Gesture Recognition

Avola,

Cinque,

Fagioli

et al. 2021

Preprint

View full text Add to dashboard Cite

Estimating the 3D hand pose from a 2D image is a well-studied problem and a requirement for several real-life applications such as virtual reality, augmented reality, and hand-gesture recognition. Currently, good estimations can be computed starting from single RGB images, especially when forcing the system to also consider, through a multi-task learning approach, the hand shape when the pose is determined. However, when addressing the aforementioned real-life tasks, performances can drop considerably depending on the hand representation, thus suggesting that stable descriptions are required to achieve satisfactory results. As a consequence, in this paper we present a keypoint-based end-to-end framework for the 3D hand and pose estimation, and successfully apply it to the hand-gesture recognition task as a study case. Specifically, after a pre-processing step where the images are normalized, the proposed pipeline comprises a multi-task semantic feature extractor generating 2D heatmaps and hand silhouettes from RGB images; a viewpoint encoder predicting hand and camera view parameters; a stable hand estimator producing the 3D hand pose and shape; and a loss function designed to jointly guide all of the components during the learning phase. To assess the proposed framework, tests were performed on a 3D pose and shape estimation benchmark dataset, obtaining state-of-the-art performances. What is more, the devised system was also evaluated on 2 hand-gesture recognition benchmark datasets, where the framework significantly outperforms other keypoint-based approaches; indicating that the presented method is an effective solution able to generate stable 3D estimates for the hand pose and shape.

show abstract

Development and Validation of an Unsupervised Feature Learning System for Leukocyte Characterization and Classification: A Multi-Hospital Study

Cited by 6 publications

References 38 publications

iCEMIGE: Integration of CEll-morphometrics, MIcrobiome, and GEne biomarker signatures for risk stratification in breast cancers

iCEMIGE: Integration of CEll-morphometrics, MIcrobiome, and GEne biomarker signatures for risk stratification in breast cancers

Feature Extraction of White Blood Cells Using CMYK-Moment Localization and Deep Learning in Acute Myeloid Leukemia Blood Smear Microscopic Images

3D Hand Pose and Shape Estimation from RGB Images for Keypoint-Based Hand Gesture Recognition

Contact Info

Product

Resources

About