Histopathological Classification of Canine Cutaneous Round Cell Tumors Using Deep Learning: A Multi-Center Study.
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ABSTRACT:
SUBMITTER: Salvi M
PROVIDER: S-EPMC8044886 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundHistopathological diagnosis of bone tumors is challenging for pathologists. We aim to classify bone tumors histopathologically in terms of aggressiveness using deep learning (DL) and compare performance with pathologists.MethodsA total of 427 pathological slides of bone tumors were produced and scanned as whole slide imaging (WSI). Tumor area of WSI was annotated by pathologists and cropped into 716,838 image patches of 256 × 256 pixels for training. After six DL models were trained and validated in patch level, performance was evaluated on testing dataset for binary classification (benign vs. non-benign) and ternary classification (benign vs. intermediate vs. malignant) in patch-level and slide-level prediction. The performance of four pathologists with different experiences was compared to the best-performing models. The gradient-weighted class activation mapping was used to visualize patch's important area.ResultsVGG-16 and Inception V3 performed better than other models in patch-level binary and ternary classification. For slide-level prediction, VGG-16 and Inception V3 had area under curve of 0.962 and 0.971 for binary classification and Cohen's kappa score (CKS) of 0.731 and 0.802 for ternary classification. The senior pathologist had CKS of 0.685 comparable to both models (p = 0.688 and p = 0.287) while attending and junior pathologists showed lower CKS than the best model (each p < 0.05). Visualization showed that the DL model depended on pathological features to make predictions.ConclusionDL can effectively classify bone tumors histopathologically in terms of aggressiveness with performance similar to senior pathologists. Our results are promising and would help expedite the future application of DL-assisted histopathological diagnosis for bone tumors.
| S-EPMC8526973 | biostudies-literature
Project description:The diagnosis and the subtyping of non-Hodgkin lymphoma (NHL) are challenging and require expert knowledge, great experience, thorough morphological analysis, and often additional expensive immunohistological and molecular methods. As these requirements are not always available, supplemental methods supporting morphological-based decision making and potentially entity subtyping are required. Deep learning methods have been shown to classify histopathological images with high accuracy, but data on NHL subtyping are limited. After annotation of histopathological whole-slide images and image patch extraction, we trained and optimized an EfficientNet convolutional neuronal network algorithm on 84,139 image patches from 629 patients and evaluated its potential to classify tumor-free reference lymph nodes, nodal small lymphocytic lymphoma/chronic lymphocytic leukemia, and nodal diffuse large B-cell lymphoma. The optimized algorithm achieved an accuracy of 95.56% on an independent test set including 16,960 image patches from 125 patients after the application of quality controls. Automatic classification of NHL is possible with high accuracy using deep learning on histopathological images and routine diagnostic applications should be pursued.
| S-EPMC8156071 | biostudies-literature
Project description:Machine learning algorithms hold the promise to effectively automate the analysis of histopathological images that are routinely generated in clinical practice. Any machine learning method used in the clinical diagnostic process has to be extremely accurate and, ideally, provide a measure of uncertainty for its predictions. Such accurate and reliable classifiers need enough labelled data for training, which requires time-consuming and costly manual annotation by pathologists. Thus, it is critical to minimise the amount of data needed to reach the desired accuracy by maximising the efficiency of training. We propose an accurate, reliable and active (ARA) image classification framework and introduce a new Bayesian Convolutional Neural Network (ARA-CNN) for classifying histopathological images of colorectal cancer. The model achieves exceptional classification accuracy, outperforming other models trained on the same dataset. The network outputs an uncertainty measurement for each tested image. We show that uncertainty measures can be used to detect mislabelled training samples and can be employed in an efficient active learning workflow. Using a variational dropout-based entropy measure of uncertainty in the workflow speeds up the learning process by roughly 45%. Finally, we utilise our model to segment whole-slide images of colorectal tissue and compute segmentation-based spatial statistics.
| S-EPMC6778075 | biostudies-literature
Project description:The last two decades have seen the emergence of a brand-new kind of music known as digital brain stimulant, also known as instrumental music or music without lyrics, which mostly comprises entrainment beats. While listening to it has the same ability to affect the brain as taking medication, it also has the risk of having a negative impact or encouraging unwanted behavior. This sparked the interest of a large number of studies in the psychological and physiological effects of music's brainwave entrainment beats on listeners. These studies started to categorize and examine how musical beats affected brainwave entrainment by looking at electroencephalogram (EEG) signals. Although this categorization represents a step forward for the early research efforts, it is constrained by the difficulty of having each musical track and conducting EEG tests on humans exposed to distortion due to noise in order to determine its influence. The work proposed in this article continues to explore this topic but in a novel, simple, accurate, and reliable categorization procedure based on the music signal elements themselves rather than dependent on EEG. VGGish and YAMNET based transfer deep learning models, are tuned to handle a straightforward, accurate real-time detector for the existence of the music beats inside music files with accuracy of 98.5 and 98.4, respectively. Despite the fact that they yield results that are equivalent, the YAMNET model is more suited for use with mobile devices due to its low power consumption and low latency. The article also proposes modified version of VGGish and YAMNET binary classifying models called BW-VGGish and BW-YAMNET respectively. The modification was to turn the binary classification into multi-classification. These multi-classifiers handle the classification of the influence of music beats (five different brain waves) on human brainwave entrainment with average accuracy of 94.5% and 94.5%, respectively. Since there was a lack of datasets addressing this kind of music, two datasets, the Brainwave Entrainment Beats (BWEB) dataset and the Brainwave Music Manipulation (BWMM) dataset, were generated for classification training and testing. The re-testing on a sample of music files that have their impact on brain waves (with their EEG) in an earlier study is done to strengthen the validity of the proposed work and to overcome the potential limitation of utilizing a music dataset that is not proved with its EEG. The success of the suggested models was demonstrated.
| S-EPMC10703030 | biostudies-literature
Project description:The term angiomatosis is used to denote a group of well-known to poorly characterized proliferative vascular entities. In animals, cutaneous progressive angiomatosis (CPA) is a disorder with variable prognosis related to the extension and depth of infiltration of the surrounding tissues by vessels. CPA may share some microscopical features with other vascular proliferations such as low-grade well-differentiated capillaritic hemangiosarcoma (HS), making the diagnosis not always straightforward, especially in small biopsies. The aim of this study is to retrospectively assess the most common diagnostic microscopical features of CPA in dogs. In this work, 11 histopathological criteria were analyzed on 31 CPA and 11 primary cutaneous HS in dogs. Features significantly associated with CPA included: lobular growth, interposition of connective tissue and adnexa between the vascular proliferation, presence of nerve fibers, and a mixed vascular proliferative component. Absence of plump/prominent endothelial cells, lack of atypia, and lack of mitoses were also significant factors differentiating CPA from HS. Additional distinctive findings in CPA, although with no statistical association to CPA diagnosis, were vascular shunting, absence of necrosis, and endothelial cell piling up. In conclusion, the combined use of different microscopical clues allowed for the distinction of CPA from HS and was considered useful for the diagnosis of CPA.
| S-EPMC9318656 | biostudies-literature
Project description:Enzymes are critical proteins in every organism. They speed up essential chemical reactions, help fight diseases, and have a wide use in the pharmaceutical and manufacturing industries. Wet lab experiments to figure out an enzyme's function are time consuming and expensive. Therefore, the need for computational approaches to address this problem are becoming necessary. Usually, an enzyme is extremely specific in performing its function. However, there exist enzymes that can perform multiple functions. A multi-functional enzyme has vast potential as it reduces the need to discover/use different enzymes for different functions. We propose an approach to predict a multi-functional enzyme's function up to the most specific fourth level of the hierarchy of the Enzyme Commission (EC) number. Previous studies can only predict the function of the enzyme till level 1. Using a dataset of 2,583 multi-functional enzymes, we achieved a hierarchical subset accuracy of 71.4% and a Macro F1 Score of 96.1% at the fourth level. The robustness of the network was further tested on a multi-functional isoforms dataset. Our method is broadly applicable and may be used to discover better enzymes. The web-server can be freely accessed at http://hecnet.cbrlab.org/.
| S-EPMC8376422 | biostudies-literature
Project description:Jointly identifying brain diseases and predicting clinical scores have attracted increasing attention in the domain of computer-aided diagnosis using magnetic resonance imaging (MRI) data, since these two tasks are highly correlated. Although several joint learning models have been developed, most existing methods focus on using human-engineered features extracted from MRI data. Due to the possible heterogeneous property between human-engineered features and subsequent classification/regression models, those methods may lead to sub-optimal learning performance. In this paper, we propose a deep multi-task multi-channel learning (DM2L) framework for simultaneous classification and regression for brain disease diagnosis, using MRI data and personal information (i.e., age, gender, and education level) of subjects. Specifically, we first identify discriminative anatomical landmarks from MR images in a data-driven manner, and then extract multiple image patches around these detected landmarks. A deep multi-task multi-channel convolutional neural network is then developed for joint disease classification and clinical score regression. We train our model on a large multi-center cohort (i.e., ADNI-1) and test it on an independent cohort (i.e., ADNI-2). Experimental results demonstrate that DM2L is superior to the state-of-the-art approaches in brain diasease diagnosis.
| S-EPMC5942232 | biostudies-literature
Project description:BackgroundTo develop a deep learning model to classify primary bone tumors from preoperative radiographs and compare performance with radiologists.MethodsA total of 1356 patients (2899 images) with histologically confirmed primary bone tumors and pre-operative radiographs were identified from five institutions' pathology databases. Manual cropping was performed by radiologists to label the lesions. Binary discriminatory capacity (benign versus not-benign and malignant versus not-malignant) and three-way classification (benign versus intermediate versus malignant) performance of our model were evaluated. The generalizability of our model was investigated on data from external test set. Final model performance was compared with interpretation from five radiologists of varying level of experience using the Permutations tests.FindingsFor benign vs. not benign, model achieved area under curve (AUC) of 0•894 and 0•877 on cross-validation and external testing, respectively. For malignant vs. not malignant, model achieved AUC of 0•907 and 0•916 on cross-validation and external testing, respectively. For three-way classification, model achieved 72•1% accuracy vs. 74•6% and 72•1% for the two subspecialists on cross-validation (p = 0•03 and p = 0•52, respectively). On external testing, model achieved 73•4% accuracy vs. 69•3%, 73•4%, 73•1%, 67•9%, and 63•4% for the two subspecialists and three junior radiologists (p = 0•14, p = 0•89, p = 0•93, p = 0•02, p < 0•01 for radiologists 1-5, respectively).InterpretationDeep learning can classify primary bone tumors using conventional radiographs in a multi-institutional dataset with similar accuracy compared to subspecialists, and better performance than junior radiologists.FundingThe project described was supported by RSNA Research & Education Foundation, through grant number RSCH2004 to Harrison X. Bai.
| S-EPMC7689511 | biostudies-literature
Project description:In the field of computer-aided Alzheimer's disease (AD) diagnosis, jointly identifying brain diseases and predicting clinical scores using magnetic resonance imaging (MRI) have attracted increasing attention since these two tasks are highly correlated. Most of existing joint learning approaches require hand-crafted feature representations for MR images. Since hand-crafted features of MRI and classification/regression models may not coordinate well with each other, conventional methods may lead to sub-optimal learning performance. Also, demographic information (e.g., age, gender, and education) of subjects may also be related to brain status, and thus can help improve the diagnostic performance. However, conventional joint learning methods seldom incorporate such demographic information into the learning models. To this end, we propose a deep multi-task multi-channel learning (DM 2L) framework for simultaneous brain disease classification and clinical score regression, using MRI data and demographic information of subjects. Specifically, we first identify the discriminative anatomical landmarks from MR images in a data-driven manner, and then extract multiple image patches around these detected landmarks. We then propose a deep multi-task multi-channel convolutional neural network for joint classification and regression. Our DM 2L framework can not only automatically learn discriminative features for MR images, but also explicitly incorporate the demographic information of subjects into the learning process. We evaluate the proposed method on four large multi-center cohorts with 1984 subjects, and the experimental results demonstrate that DM 2L is superior to several state-of-the-art joint learning methods in both the tasks of disease classification and clinical score regression.
| S-EPMC6764421 | biostudies-literature
Project description:Collar-mounted canine activity monitors can use accelerometer data to estimate dog activity levels, step counts, and distance traveled. With recent advances in machine learning and embedded computing, much more nuanced and accurate behavior classification has become possible, giving these affordable consumer devices the potential to improve the efficiency and effectiveness of pet healthcare. Here, we describe a novel deep learning algorithm that classifies dog behavior at sub-second resolution using commercial pet activity monitors. We built machine learning training databases from more than 5000 videos of more than 2500 dogs and ran the algorithms in production on more than 11 million days of device data. We then surveyed project participants representing 10,550 dogs, which provided 163,110 event responses to validate real-world detection of eating and drinking behavior. The resultant algorithm displayed a sensitivity and specificity for detecting drinking behavior (0.949 and 0.999, respectively) and eating behavior (0.988, 0.983). We also demonstrated detection of licking (0.772, 0.990), petting (0.305, 0.991), rubbing (0.729, 0.996), scratching (0.870, 0.997), and sniffing (0.610, 0.968). We show that the devices' position on the collar had no measurable impact on performance. In production, users reported a true positive rate of 95.3% for eating (among 1514 users), and of 94.9% for drinking (among 1491 users). The study demonstrates the accurate detection of important health-related canine behaviors using a collar-mounted accelerometer. We trained and validated our algorithms on a large and realistic training dataset, and we assessed and confirmed accuracy in production via user validation.
| S-EPMC8228965 | biostudies-literature