Breast Tumour Classification

PatchCamelyon is an image classification dataset. It consists of 327.680 color images (96 x 96px) extracted from histopathologic scans of lymph node sections. Each image is annotated with a binary label indicating presence of metastatic tissue. PCam provides a new benchmark for machine learning models: bigger than CIFAR10, smaller than ImageNet, trainable on a single GPU.

Breast cancer (BC) has become the greatest threat to women’s health worldwide. Clinically, identification of axillary lymph node (ALN) metastasis and other tumor clinical characteristics such as ER, PR, and so on, are important for evaluating the prognosis and guiding the treatment for BC patients. Several studies intended to predict the ALN status and other tumor clinical characteristics by clinicopathological data and genetic testing score. However, due to the relatively poor predictive values and high genetic testing costs, these methods are often limited. Recently, deep learning (DL) has enabled rapid advances in computational pathology, DL can perform high-throughput feature extraction on medical images and analyze the correlation between primary tumor features and above status. So far, there is no relevant research on preoperatively predicting ALN metastasis and other tumor clinical characteristics based on WSIs of primary BC samples. Our paper has introduced a new dataset of Early Breast Cancer Core-Needle Biopsy WSI (BCNB), which includes core-needle biopsy whole slide images (WSIs) of early breast cancer patients and the corresponding clinical data. The WSIs have been examined and annotated by two independent and experienced pathologists blinded to all patient-related information. Based on this dataset, we have studied the deep learning algorithm for predicting the metastatic status of ALN preoperatively by using multiple instance learning (MIL), and have achieved the best AUC of 0.831 in the independent test cohort. For more details, please review our paper. There are WSIs of 1058 patients, and only part of tumor regions are annotated in WSIs. Except for the WSIs, we have also provided the clinical characteristics of each patient, which includes age, tumor size, tumor type, ER, PR, HER2, HER2 expression, histological grading, surgical, Ki67, molecular subtype, number of lymph node metastases, and the metastatic status of axillary lymph node (ALN). The dataset has been desensitized, and not contained the privacy information of patients. Based on this dataset, we have studied the prediction of the metastatic status of axillary lymph node (ALN) in our paper, which is a weakly supervised classification task. However, other researches based on our dataset are also feasible, such as the prediction of histological grading, molecular subtype, HER2, ER, and PR. We do not limit the specific content for your research, and any research based on our dataset is welcome. Please note that the dataset is only used for education and research, and the usage for commercial and clinical applications is not allowed. The usage of this dataset must follow the license.

The breast lesion detection in ultrasound videos dataset uses a clip-level and video-level feature aggregated network (CVA-Net) and consists of 188 ultrasound videos, of which 113 are labeled malignant and 75 benign. Overall these consist of 25,272 ultrasound images in total with the number of images for each video varying from 28 to 413. 150 videos were used for training, 38 for testing. The primary intended use case would be for computer-aided breast cancer diagnosis, supporting systems to assist radiologists. Here are more details summarising the approach: * A novel network: a new state-of-the-art clip-level and video level feature aggregated network (CVA-Net) created to aggregate clip-level temporal features and video-level lesion classification features to fuse and into a prediction classifier. It outperformed existing methods mainly focused on 2D images and or fusing with unlabeled videos. * The need for increased accuracy contributed to the motivation, given detection challenges due to blurry breast lesion boundaries, inhomogeneous distributions, changeable breast lesion sizes, and positions in dynamic video. * Each video has a complete scan of the tumor, from where it becomes visible to where it is no longer visible as well as the largest section - all acquired via LOGIQ-E9 and PHILIPS TIS L9-3 ultrasound machines . * 2 pathologists with 8 years of experience invited to manually annotate the breast lesion rectangles inside each frame and give the corresponding classification. Second source in addition to separate homepage URL below: https://github.com/jhl-Det/CVA-Net/tree/main/datasets

Several datasets are fostering innovation in higher-level functions for everyone, everywhere. By providing this repository, we hope to encourage the research community to focus on hard problems. In this repository, we present the real results severity (BIRADS) and pathology (post-report) classifications provided by the Radiologist Director from the Radiology Department of Hospital Fernando Fonseca while diagnosing several patients (see dataset-uta4-dicom) from our User Tests and Analysis 4 (UTA4) study. Here, we provide a dataset for the measurements of both severity (BIRADS) and pathology classifications concerning the patient diagnostic. Work and results are published on a top Human-Computer Interaction (HCI) conference named AVI 2020 (page). Results were analyzed and interpreted from our Statistical Analysis charts. The user tests were made in clinical institutions, where clinicians diagnose several patients for a Single-Modality vs Multi-Modality comparison. For example, in these tests, we used both prototype-single-modality and prototype-multi-modality repositories for the comparison. On the same hand, the hereby dataset represents the pieces of information of both BreastScreening and MIDA projects. These projects are research projects that deal with the use of a recently proposed technique in literature: Deep Convolutional Neural Networks (CNNs). From a developed User Interface (UI) and framework, these deep networks will incorporate several datasets in different modes. For more information about the available datasets please follow the Datasets page on the Wiki of the meta information repository. Last but not least, you can find further information on the Wiki in this repository. We also have several demos to see in our YouTube Channel, please follow us.