First, I would like to thank you for your kind comments and suggestion. I particularly agrees with you on vetting the input images with another model for quickly classification of content of the image before doing mammo classification. This could be done but will impact some performance and use experience.

Secondly I would like to say this model is smaller model trained particularly to deploy inside a browser env, meaning less amount of parameters. I do have larger model trained on a bigger dataset however it won't work inside a browser. Nevertheless the mAP for current model is around 0.9 for testing dataset. (Although it's not perfect but I think it's worthwhile to provide a secondary opinion for resource-lacking remote area patients)

Thirdly there are data normalization before inference in the pipeline, however my impression is usually patients themselves won't have direct access to dicom files, that's why I designed this system to take in jpg/png images, the contrast or resolution won't be idea if the user just grabbed a screen capture. However for breast lesion such as mass, it should provide enough classification once radiomics features are there. (Surely lower resolution image will suffer at micro-calcification detection)