Men Hair Loss Segmentation Dataset

It is a large-scale medical conversation dataset with 1,760 hours of audio recordings, featuring audio of medical calls (inbound/outbound), including med device promo, medical dictation, product orders, and patient-doctor conversations, all paired with structured transcripts. This medical speech dataset includes MP3 and WAV formats with JSON and DOCX transcriptions, providing high-quality annotated data.

This hair loss dataset provides alopecia images of women captured from the top and front sides, labeled into three classes based on the Ludwig scale, with metadata (age, gender, ethnicity) to support machine learning, deep learning, and AI models for hair loss detection, early diagnosis, and research on hair thinning and scalp health.

This hair loss dataset features annotated images of women with varying degrees of hair loss, designed for machine learning and deep learning applications focused on analyzing hair density, scalp conditions, and developing solutions for hair restoration, hair thinning, and alopecia treatment

The hair loss dataset contains high-resolution scalp images of people captured from five sides, labeled with seven classes on the Norwood-Hamilton scale and supplemented with hair follicle annotations to help machine learning models analyze hair density, thinning patterns, and diagnose baldness

OCT dataset features 1,000 volumetric OCT scans (B-scans) of the retina with annotated layers, fluid regions, and clinical diagnoses, including AMD, diabetic retinopathy, and glaucoma. This DiCOM dataset supports AI model training in disease classification, pathology segmentation, and computer vision–based retinal analysis using coherence tomography images.

This large-scale X-ray labeled images dataset comprises over 1.2 million digital X-ray images focused on the shoulder, elbow, and wrist joints, designed for pathology recognition, segmentation tasks, and training deep learning models in medical imaging, with detailed annotations for the accurate detection of bone and joint conditions.

Mammography Dataset is a comprehensive DDSM dataset containing over 600,000 digital mammograms with pathology segmentation and study-level labels, designed for cancer detection, breast imaging, and training AI models in cancer diagnosis using real-world mammographic images from screening mammography exams.

This brain CT dataset comprises over 70,000 DICOM studies with labeled pathologies such as intracerebral hemorrhage, ischemic stroke, and vessel abnormalities, providing segmentation data and annotations to support medical research, computer vision, and deep learning–based diagnostic algorithms.

It is a large-scale CT scan chest dataset featuring over 150,000 chest CT images with annotated pathologies, designed for training deep learning models in lung disease detection, cancer diagnosis, and medical imaging tasks, with labeled data covering a wide range of conditions such as pulmonary embolism, tuberculosis, and lung cancer.

It is a detailed brain CT dataset featuring over 1,000 annotated CT scans for tumor segmentation, brain hemorrhage detection, and other pathology classification tasks, designed to train machine learning models in semantic segmentation, treatment planning, and accurate analysis of brain tissues, structures, and injuries in medical imaging.