deepliif.org

What It Is

DeepLIIF (Deep-Learning Inferred Multiplex Immunofluorescence) is an AI model for virtual restaining and quantification of standard immunohistochemistry (IHC) images. The site provides a web upload interface where users can drag and drop IHC images for processing, and it also includes a DP4ALL video stitching feature. Its positioning is clearly geared toward research and medical image analysis rather than general-purpose vision AI.

Core Capabilities and How to Use It

Based on the captured page text, DeepLIIF supports IHC image quantification and provides sample images for users to test directly, including marker-related images such as Ki67 and CD3. During upload, users can choose 10X, 20X, or 40X resolution, and the page recommends using 10X or higher for better results. The maximum image size is limited to 4096 x 4096 pixels, and it supports more than 150 Bio-Formats-compatible image formats. Video stitching supports MP4, AVI, and MOV files, with a size limit of under 3GB.

Pricing, Privacy, and Integration

The page does not disclose its pricing model, free quota, trial limits, or commercial licensing terms, so long-term usage costs cannot be assessed. On privacy, the site explicitly warns users not to upload images containing PHI or PII, which is an important risk notice. However, the text does not further explain whether data is stored, how long it is retained, whether it is encrypted, or whether it complies with medical data regulations such as HIPAA. In terms of integration, the only confirmed point is support for Bio-Formats image formats; there is no visible information about APIs, SDKs, or integration with pathology systems/LIMS.

Pros and Cons

Its strengths are its focused use case and direct support for IHC image quantification. The web upload workflow is simple, and sample images make it easy for researchers to try it quickly. Format compatibility is also relatively strong. The limitations are insufficient disclosure of key information: there are no model performance metrics, validation datasets, stated applicability boundaries, pricing details, or service support information. In addition, the image size limit may not be suitable for directly processing very large whole-slide images.

Who It’s For and Access from China

DeepLIIF is better suited for researchers in pathology, tumor immunohistochemistry, and medical imaging AI for preliminary analysis or method validation. If clinical samples or sensitive health data are involved, users should first confirm its compliance status and data processing policies. The captured text does not provide information on access from mainland China, and payment methods are also unknown. If access is unstable, local pathology image analysis software, open-source deep learning models, or internally deployed institutional solutions may be considered as alternatives.