This dataset comprises 325 histopathological images of clear cell renal cell carcinoma (ccRCC) tissue sections, designed to characterize vascular morphology based on CD31 immunohistochemical staining. Each image was scanned at 10× magnification and annotated with global proportions for three distinct vascular patterns: high-branching (HB), low-branching (LB), and sinusoidal (SN). The provided annotations include the relative distribution of each vascular class per image.
This dataset includes conjunctival and retinal images collected from both diabetic and healthy individuals to support research on diabetes-related vascular changes. For each subject, eight conjunctival images (four per eye: looking left, right, up, and down) are provided. Subjects with diabetes additionally have corresponding left and right retinal fundus images. Metadata for diabetic participants includes classification into subgroups: diabetes only, diabetes with retinopathy, or diabetes with related complications such as hypertension.
<p>Electrocardiogram (ECG) interpretation is critical for diagnosing a wide range of cardiovascular conditions. To streamline and accelerate the development of deep learning models in this domain, we present a novel, image-based version of the PTB Diagnostic ECG Database tailored for use with convolutional neural networks (CNNs), vision transformers (ViTs), and other image classification architectures. This enhanced dataset consists of 516 grayscale .png images, each representing a 12-lead ECG signal arranged as a 2D matrix (12 × T, where T is the number of time steps).
Paper : Assessment of Inference Improvements for Facial Micronutrient Deficiency Detection using Attention-Enhanced YOLOv5
Authors : Amey Agarwal, Shreya Rathod, Riva Rodrigues, Nirmitee Sarode, Dhananjay R. Kalbande
Desciption
This is a dataset of 7 classes : 6 facial skin problems and 1 null class.
A facial skin problem may be identified in an image and marked using Bounding Box Annotation.
Acne Class indicates deficiency of Vitamin D
Blackhead and Nodules are types of acne
The Breast Cancer Histopathological Image Classification (BreakHis) is composed of 9,109 microscopic images of breast tumor tissue collected from 82 patients using different magnifying factors (40X, 100X, 200X, and 400X). To date, it contains 2,480 benign and 5,429 malignant samples (700X460 pixels, 3-channel RGB, 8-bit depth in each channel, PNG format).
Speckle contrast optical spectroscopy (SCOS) is an optical technique capable of measuring human cerebral blood flow and brain function non-invasively. Its tomographic extension, speckle contrast optical tomography (SCOT), can provide blood flow variation maps with measurements using overlapping source-detector channel pairs. Linearity is often assumed in most image reconstruction methods, but non-linearity could exist in the relations between measured signals and blood flow variations.
This 3DTeethSegX dataset is a benchmark dataset specifically designed for tooth point cloud completion and segmentation tasks. Built upon the publicly available 3DTeethSeg 2022 MICCAI Challenge dataset, it comprises 1,494 pairs of tooth point clouds and their corresponding tooth images from 38 patients. Each pair includes a partial point cloud (2,048 points) and a complete point cloud (16,384 points).
Brain tumors are one of the most common diseases threatening human health. Early detection and precise segmentation are of great significance for clinical diagnosis and treatment. This paper presents a Learnable Wavelet Transform and Attention Mechanism network(LWTA-Net2D), based on 2D Convolutional Neural Networks (CNN), integrating Learnable Discrete Wavelet Transform (LDWT), combination of Monte Carlo Attention (MCattn) and Monte Carlo Bottleneck Layer (MCBottleneck), and a U-Net-based encoder-decoder architecture.
To enhance the application of radiotherapy planning, we developed a novel head and neck imaging dataset, HND, comprising simulation X-ray computed tomography (CT) images from 486 patients with head and neck cancers who underwent intensity-modulated radiotherapy (IMRT) between February 2019 and June 2024 (Table 1).
