google/medsiglip-448

Zero-Shot Image Classification • 0.9B • Updated Jul 10 • 12.8k • 90

Note MedSigLIP is a SigLIP variant that is trained to encode medical images and text into a common embedding space. It was trained on a variety of de-identified medical image and text pairs, including chest X-rays, dermatology images, ophthalmology images, histopathology slides, and slices of CT and MRI volumes, along with associated descriptions or reports.

google/medasr

Automatic Speech Recognition • 0.1B • Updated about 3 hours ago • 823 • 15

Note MedASR is a speech-to-text model based on the Conformer architecture that is pre-trained for medical dictation and transcription.

google/hear-pytorch

Image Feature Extraction • Updated May 7 • 240 • 11

Note Health Acoustic Representations accelerates AI development for bioacoustic data e.g., coughs or breath sounds. The model is pre-trained on 300 million 2-second audio clips to produce embeddings that capture dense features relevant for bioacoustic applications.

google/hear

Updated Apr 17 • 83 • 31

Note Health Acoustic Representations accelerates AI development for bioacoustic data e.g., coughs or breath sounds. The model is pre-trained on 300 million 2-second audio clips to produce embeddings that capture dense features relevant for bioacoustic applications.

google/path-foundation

Image Classification • Updated Feb 20 • 137 • 56

Note Path Foundation accelerates AI development for histopathology image analysis. The model uses self-supervised learning on large amounts of digital pathology data to produce embeddings that capture dense features relevant for histopathology applications.

google/derm-foundation

Image Classification • Updated Apr 1 • 254 • 74

Note Derm Foundation accelerates AI development for skin image analysis. The model is pre-trained on large amounts of labeled skin images to produce embeddings that capture dense features relevant for dermatology applications.

google/cxr-foundation

Image Classification • Updated Feb 20 • 104 • 94

Note CXR Foundation accelerates AI development for chest X-ray image analysis. The model is pre-trained on large amounts of chest X-rays paired with radiology reports. It produces language-aligned embeddings that capture dense features relevant for chest X-ray applications.