Augmented diagnosis, quantification, characterization, image reconstruction: medical artificial intelligence at your fingertips for radiology


AI solution for breast cancer screening and diagnosis

Transpara supports radiologists in their interpretation of a mammography exam. Transpara gives a probability of breast cancer presence based on lesions automatically detected and qualified with AI

Automatic detection of lesions : soft tissue lesions and microcalcifications

Segmentation and contouring of suspicous regions.

Evaluation of malignancy level for every lesion detected

Definition of a regional score for every suspicious micro calcification and tissue lesion.

Analysis of the overall likelihood that cancer is present in a mammogram

Categorization of the exam based on a global score.

  • breast cancer
  • mammography
  • soft tissue lesion
  • microcalcification
  • tomosynthesis
  • 3D
  • lesion

Increase confidence in your diagnosis

Increase confidence in your diagnosis for both normal and suspicious cases thanks to an AI double reading

Add another layer of safety

Add another layer of safety thanks to alerts given by global exam scores

Identify suspicious lesions faster

Identify and qualify benign or malign lesions faster in suspecious regions

Save time, reassure your diagnosis and streamline your workflow with Incepto


Quand je vois un score 1 ou 2, même sur des seins denses, je suis très rassuré. Évidemment, j’analyse la mammographie avec beaucoup d’attention, mais de façon plus sereine et plus rapide que ce que je faisais jusqu’à présent, ce qui me laisse plus de temps pour porter mon attention sur des résultats moins favorables. A contrario, quand les scores sont de 8, 9, et 10, je m’appuie sur les anomalies indiquées par Transpara et suis encore plus vigilant dans mon analyse.
Dr Marc Abehsera,

Senologist and radiologist
Paris American Hospital


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  2. Sasaki, M. et al. Artificial intelligence for breast cancer detection in mammography: experience of use of the ScreenPoint Medical Transpara system in 310 Japanese women. Breast Cancer 27, 642–651 (2020).
  3. Lång, K. et al. Identifying normal mammograms in a large screening population using artificial intelligence. Eur Radiol (2020) doi:10.1007/s00330-020-07165-1.
  4. Rodriguez-Ruiz, A. et al. Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study. Eur Radiol 29, 4825–4832 (2019).
  5. Rodriguez-Ruiz, A. et al. Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. 7 (2019).
  6. Le, E. P. V., Wang, Y., Huang, Y., Hickman, S. & Gilbert, F. J. Artificial intelligence in breast imaging. Clinical Radiology 74, 357–366 (2019).
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  8. Rodríguez-Ruiz, A. et al. Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System. Radiology 181371 (2018) doi:10.1148/radiol.2018181371.
  9. Hupse, R. et al. Computer-aided Detection of Masses at Mammography: Interactive Decision Support versus Prompts. Radiology 266, 123–129 (2013).

Save time, secure diagnosis and optimize your workflow with Incepto