Clinical diagnosis of the eye is performed over multifarious data modalities including scalar clinical labels, vectorized biomarkers, two-dimensional fundus images, and three-dimensional Optical Coherence Tomography (OCT) scans. While the clinical labels, fundus images and OCT scans are instrumental measurements, the vectorized biomarkers are interpreted attributes from the other measurements. Clinical practitioners use all these data modalities for diagnosing and treating eye diseases like Diabetic Retinopathy (DR) or Diabetic Macular Edema (DME). Enabling usage of machine learning algorithms within the ophthalmic medical domain requires research into the relationships and interactions between these relevant data modalities. Existing datasets are limited in that: (i) they view the problem as disease prediction without assessing biomarkers, and (ii) they do not consider the explicit relationship among all four data modalities over the treatment period. In this paper, we introduce the Ophthalmic Labels for Investigating Visual Eye Semantics (OLIVES) dataset that addresses the above limitations. This is the first OCT and fundus dataset that includes clinical labels, biomarker labels, and time-series patient treatment information from associated clinical trials. The dataset consists of 1268 fundus eye images each with 49 OCT scans, and 16 biomarkers, along with 3 clinical labels and a disease diagnosis of DR or DME. In total, there are 96 eyes' data averaged over a period of at least two years with each eye treated for an average of 66 weeks and 7 injections. OLIVES dataset has advantages in other fields of machine learning research including self-supervised learning as it provides alternate augmentation schemes that are medically grounded.
LymphoMNIST is a comprehensive dataset designed for the nuanced classification of lymphocyte images. It encompasses approximately 80,000 high-resolution 64x64 images, meticulously categorized into three primary classes: B cells, T4 cells, and T8 cells. Dataset Characteristics: Size: ~80,000 images Resolution: 64x64 pixels Classes: B cells, T4 cells, T8 cells Format: MNIST-like standardized biomedical imagery Modality: Microscopy-based high-resolution cell images Motivation and Summary: LymphoMNIST aims to bridge the gap in biomedical image analysis by providing a dataset that is vast in scale and rich in detail. It supports a wide array of research endeavors, from fundamental biological studies to advanced computational model development. Potential Use Cases: Medical Research: Studying lymphocyte morphology and characteristics Machine Learning & AI: Developing and evaluating image classification models AutoML & Benchmarking: Serving as a benchmark dataset for automated model training and performance evaluation Educational Purposes: Teaching deep learning concepts in biomedical imaging Data Collection Process: The dataset comprises high-resolution images of lymphocytes obtained through microscopy. Each image is standardized to a 64x64 pixel resolution to maintain consistency and facilitate analysis. Annotations and Labels: Each image is labeled as one of the three lymphocyte classes: B cells, T4 cells, or T8 cells. The labeling process was conducted by experts in the field to ensure accuracy.