Publications

Fine needle aspiration (FNA) biopsy of thyroid nodules is a safe, cost-effective, and accurate diagnostic method for detecting thyroid cancer. However, about 10% of initial FNA biopsy samples from patients are non-diagnostic and require repeated FNA, which delays the diagnosis and appropriate care. On-site evaluation of the FNA sample can be performed to filter out non-diagnostic FNA samples. Unfortunately, it involves a time-consuming staining process, and a cytopathologist has to be present at the time of FNA. To bypass the staining process and expert interpretation of FNA specimens at the clinics, we developed a deep learning-based ensemble model termed FNA-Net that allows in situ screening of adequacy of unstained thyroid FNA samples smeared on a glass slide which can decrease the non-diagnostic rate in thyroid FNA. FNA-Net combines two deep learning models, a patch-based whole slide image classifier and Faster R-CNN, to detect follicular clusters with high precision. Then, FNA-Net classifies sample slides to be non-diagnostic if the total number of detected follicular clusters is less than a predetermined threshold. With bootstrapped sampling, FNA-Net achieved a 0.81 F1 score and 0.84 AUC in the precision-recall curve for detecting the non-diagnostic slides whose follicular clusters are less than six. We expect that FNA-Net can dramatically reduce the diagnostic cost associated with FNA biopsy and improve the quality of patient care. *Co-corresponding authors

Analyzing the dynamic changes of cellular morphology is important for understanding the various functions and characteristics of live cells, including stem cells and metastatic cancer cells. To this end, we need to track all points on the highly deformable cellular contour in every frame of live cell video. Local shapes and textures on the contour are not evident, and their motions are complex, often with expansion and contraction of local contour features. The prior arts for optical flow or deep point set tracking are unsuited due to the fluidity of cells, and previous deep contour tracking does not consider point correspondence. We propose the first deep learning-based tracking of cellular (or more generally viscoelastic materials) contours with point correspondence by fusing dense representation between two contours with cross attention. Since it is impractical to manually label dense tracking points on the contour, unsupervised learning comprised of the mechanical and cyclical consistency losses is proposed to train our contour tracker. The mechanical loss forcing the points to move perpendicular to the contour effectively helps out. For quantitative evaluation, we labeled sparse tracking points along the contour of live cells from two live cell datasets taken with phase contrast and confocal fluorescence microscopes. Our contour tracker quantitatively outperforms compared methods and produces qualitatively more favorable results. Our code and data are publicly available at this https URL Project page: https://junbongjang.github.io/projects/contour-tracking/index.html

Fine needle aspiration (FNA) biopsy of thyroid nodules is a safe, cost-effective, and accurate diagnostic method for detecting thyroid cancer. However, about 10% of initial FNA biopsy samples from patients are non-diagnostic and require repeated FNA, which delays the diagnosis and appropriate care. On-site evaluation of the FNA sample can be performed to filter out non-diagnostic FNA samples. Unfortunately, it involves a time-consuming staining process, and a cytopathologist has to be present at the time of FNA. To bypass the staining process and expert interpretation of FNA specimens at the clinics, we developed a deep learning-based ensemble model termed FNA-Net that allows in situ screening of adequacy of unstained thyroid FNA samples smeared on a glass slide which can decrease the non-diagnostic rate in thyroid FNA. FNA-Net combines two deep learning models, a patch-based whole slide image classifier and Faster R-CNN, to detect follicular clusters with high precision. Then, FNA-Net classifies sample slides to be non-diagnostic if the total number of detected follicular clusters is less than a predetermined threshold. With bootstrapped sampling, FNA-Net achieved a 0.81 F1 score and 0.84 AUC in the precision-recall curve for detecting the non-diagnostic slides whose follicular clusters are less than six. We expect that FNA-Net can dramatically reduce the diagnostic cost associated with FNA biopsy and improve the quality of patient care. *Co-corresponding authors

Background Deep learning has been successfully applied to ECG data to aid in the accurate and more rapid diagnosis of acutely decompensated heart failure (ADHF). Previous applications focused primarily on classifying known ECG patterns in well-controlled clinical settings. However, this approach does not fully capitalize on the potential of deep learning, which directly learns important features without relying on a priori knowledge. In addition, deep learning applications to ECG data obtained from wearable devices have not been well studied, especially in the field of ADHF prediction. Methods We used ECG and transthoracic bioimpedance data from the SENTINEL-HF study, which enrolled patients (≥21 years) who were hospitalized with a primary diagnosis of heart failure or with ADHF symptoms. To build an ECG-based prediction model of ADHF, we developed a deep cross-modal feature learning pipeline, termed ECGX-Net, that utilizes raw ECG time series and transthoracic bioimpedance data from wearable devices. To extract rich features from ECG time series data, we first adopted a transfer learning approach in which ECG time series were transformed into 2D images, followed by feature extraction using ImageNet-pretrained DenseNet121/VGG19 models. After data filtering, we applied cross-modal feature learning in which a regressor was trained with ECG and transthoracic bioimpedance. Then, we concatenated the DenseNet121/VGG19 features with the regression features and used them to train a support vector machine (SVM) without bioimpedance information. Results The high-precision classifier using ECGX-Net predicted ADHF with a precision of 94 %, a recall of 79 %, and an F1-score of 0.85. The high-recall classifier with only DenseNet121 had a precision of 80 %, a recall of 98 %, and an F1-score of 0.88. We found that ECGX-Net was effective for high-precision classification, while DenseNet121 was effective for high-recall classification. Conclusion We show the potential for predicting ADHF from single-channel ECG recordings obtained from outpatients, enabling timely warning signs of heart failure. Our cross-modal feature learning pipeline is expected to improve ECG-based heart failure prediction by handling the unique requirements of medical scenarios and resource limitations. *Co-first authors: X. Pan and C. Wang. **Co-corresponding authors: K. Chon, Y. Mendelson, and K. Lee

Quantitative studies of cellular morphodynamics rely on accurate cell segmentation in live cell images. However, fluorescence and phase contrast imaging hinder accurate edge localization. To address this challenge, we developed MARS-Net, a deep learning model integrating ImageNet-pretrained VGG19 encoder and U-Net decoder trained on the datasets from multiple types of microscopy images. Here, we provide the protocol for installing MARS-Net, labeling images, training MARS-Net for edge localization, evaluating the trained models’ performance, and performing the quantitative profiling of cellular morphodynamics.

Quantitative studies of cellular morphodynamics rely on accurate cell segmentation in live cell images. However, fluorescence and phase contrast imaging hinder accurate edge localization. To address this challenge, we developed MARS-Net, a deep learning model integrating ImageNet-pretrained VGG19 encoder and U-Net decoder trained on the datasets from multiple types of microscopy images. Here, we provide the protocol for installing MARS-Net, labeling images, training MARS-Net for edge localization, evaluating the trained models’ performance, and performing the quantitative profiling of cellular morphodynamics.

Indoor flooding is a leading contributor to indoor dampness and the associated mold infestations in the coastal United States. Whether the prevalent mold genera that infest the coastal flood-prone buildings are different from those not flood-prone is unknown. In the current case study of 28 mold-infested buildings across the U.S. east coast, we surprisingly noted a trend of higher prevalence of indoor Aspergillus and Penicillium genera (denoted here as Asp–Pen) in buildings with previous flooding history. Hence, we sought to determine the possibility of a potential statistically significant association between indoor Asp–Pen prevalence and three building-related variables: (i) indoor flooding history, (ii) geographical location, and (iii) the building s use (residential versus non-residential). Culturable spores and hyphal fragments in indoor air were collected using the settle-plate method, and corresponding genera were confirmed using phylogenetic analysis of their ITS sequence (the fungal barcode). Analysis of variance (ANOVA) using Generalized linear model procedure (GLM) showed that Asp–Pen prevalence is significantly associated with indoor flooding as well as coastal proximity. To address the small sample size, a multivariate decision tree analysis was conducted, which ranked indoor flooding history as the strongest determinant of Asp–Pen prevalence, followed by geographical location and the building s use.

Cell cycle control is a key aspect of numerous physiological and pathological processes. The contribution of biophysical cues, such as stiffness or elasticity of the underlying extracellular matrix (ECM), is critically important in regulating cell cycle progression and proliferation. Indeed, increased ECM stiffness causes aberrant cell cycle progression and proliferation. However, the molecular mechanisms that control these stiffness-mediated cellular responses remain unclear. Here, we address this gap and show good evidence that lamellipodin, previously known as a critical regulator of cell migration, stimulates ECM stiffness-mediated cyclin expression and intracellular stiffening. We observed that increased ECM stiffness upregulates lamellipodin expression. This is mediated by an integrin-dependent FAK-Cas-Rac signaling module and supports stiffness-mediated lamellipodin induction. Mechanistically, we find that lamellipodin overexpression increased and lamellipodin knockdown reduced stiffness-induced cell cyclin expression and cell proliferation, and intracellular stiffness. Overall, these results suggest that lamellipodin levels may be critical for regulating cell proliferation.

Cells respond heterogeneously to molecular and environmental perturbations. Phenotypic heterogeneity, wherein multiple phenotypes coexist in the same conditions, presents challenges when interpreting the observed heterogeneity. Advances in live cell microscopy allow researchers to acquire an unprecedented amount of live cell image data at high spatiotemporal resolutions. Phenotyping cellular dynamics, however, is a nontrivial task and requires machine learning (ML) approaches to discern phenotypic heterogeneity from live cell images. In recent years, ML has proven instrumental in biomedical research, allowing scientists to implement sophisticated computation in which computers learn and effectively perform specific analyses with minimal human instruction or intervention. In this review, we discuss how ML has been recently employed in the study of cell motility and morphodynamics to identify phenotypes from computer vision analysis. We focus on new approaches to extract and learn meaningful spatiotemporal features from complex live cell images for cellular and subcellular phenotyping.

Indoor flooding is a leading contributor to indoor dampness and the associated mold infestations in the coastal United States. Whether the prevalent mold genera that infest the coastal flood-prone buildings are different from those not flood-prone is unknown. In the current case study of 28 mold-infested buildings across the U.S. east coast, we surprisingly noted a trend of higher prevalence of indoor Aspergillus and Penicillium genera (denoted here as Asp–Pen) in buildings with previous flooding history. Hence, we sought to determine the possibility of a potential statistically significant association between indoor Asp–Pen prevalence and three building-related variables: (i) indoor flooding history, (ii) geographical location, and (iii) the building s use (residential versus non-residential). Culturable spores and hyphal fragments in indoor air were collected using the settle-plate method, and corresponding genera were confirmed using phylogenetic analysis of their ITS sequence (the fungal barcode). Analysis of variance (ANOVA) using Generalized linear model procedure (GLM) showed that Asp–Pen prevalence is significantly associated with indoor flooding as well as coastal proximity. To address the small sample size, a multivariate decision tree analysis was conducted, which ranked indoor flooding history as the strongest determinant of Asp–Pen prevalence, followed by geographical location and the building s use.