Colorectal cancer is one of the leading causes of cancer-related deaths worldwide. It is estimated to be the third most common cancer and the second leading cause of cancer death in both men and women. A development known as a polyp, which may initially be noncancerous (benign) or later turn cancerous, is how colorectal cancer typically manifests.

The everwidening uses of AI in healthcare industry are astonishing. AI can play a significant role in detecting colorectal cancer by assisting in various aspects of the screening and diagnosis process. A few very important examples are:

  • In medical imaging, AI algorithms are capable of finding polyps, lesions, and cancers.
  • Radiologists and pathologists can assess x-rays with the use of AI-powered CAD systems.
  • Using patient data, AI models can evaluate the risk of colorectal cancer.
  • For the management of colorectal cancer, AI-based decision support systems offer individualized recommendations and treatment strategies.
  • Natural Language Processing (NLP) can be used by AI systems to extract information from medical records and recognize colorectal cancer.
  • AI systems can offer individualized information about colorectal cancer, screening procedures, and dietary modifications.
Medical illustration of Colorectal Cancer – Polyp
Doctors and researchers using innovative technologies for medicine and healthcare: artificial intelligence, virtual reality, drones, stem cells and digital organs

Better workflow with HSA KIT

Over a short period of time, HS Analysis GmbH has partnered with numerous companies, hospitals and laboratories as clients which provide microscopic slides or data that needs to be analysed. The physical slides can be digitalised to whole slide images (WSI) with the help of HSA KIT software that can be integrated to commonly used microscopes, and data can be retrieved in the form of reports and atomated graphical representaions. This helps to eliminate the extensive process that is usually practiced by medical professionals.

Traditional workflow of medical image analysis is rather cumbersome where the medical images obtained from CT Scans, MRIs or X-Rays is pre-processed and analysed by a radiologist who evaluates and interprets the images to prepare a very subjective report based on his own knowledge. This might be followed up with a quality assurance by another radiologist for second opinion.

On the other hand, AI based analyses using HSA KIT would provide:

  • Standardised process with subjective/objective analysis
  • Extraction of relevant features from raw data and create meaningful representations for training AI models
  • Module selection and configuration without excessive coding
  • Easy to learn software: Annotate , Train and Automate
  • Quick and efficient analysis of multiple medical images which cuts down time for diagnosis or treament
  • Automated report generation to boost productivity and help physicians or radiologists in evaluation process

AI based analyses can be way more efficient, however it can only act as a supportive tool to human expertise and never be a complete alternative. AI algorithms have the tendency to produce false positives and false negatives, and may have trouble adapting to a data that deviates from training dataset.

Custom modules in HSA KIT

HSA KIT includes anything and everything to develop and train custom deep learning models. Our team’s ability to provide customized modules highlight their dedication to meeting individual client requirements and delivering solutions that are specifically tailored to their needs. There are currently hundreds of modules available in HSA KIT which are designed to be scalable including some long-term requirements, and building flexible solutions that can adapt and accommodate changing needs over time. Moreover, through a client-centric approach, we leverage the information from iteraction to design and deliver modules that perfectly align with clients‘ specific goals and objectives; ensuring maximum value and satisfaction.


The HSA KIT includes professional, top-notch annotation capabilities and offers zooming in to the very last pixel in the image. Existing DL models can be trained on by the clients and optimized for their purposes. Additional annotations can be readily added to the fundamental structures that are already present, in order to boost the model’s detection precision. A high-quality deep learning model will be generated as a result of improved training, thanks to the ground truth data (GTDs) that are all recorded in the database from the beginning.

Colorectal Cancer (CRC) Module in HSA KIT very efficiently categorises between three types of glands within the Base ROI:

  • Healthy
  • Adinoma
  • Carcinoma
  • Other tissues (muscle, adipose or stroma)

An example of various glands within Base Roi.

An exapmle of detecting Carcinoma and Adinoma.

Zoom out view: Numerous healthy glands detected.

Zoom out view: Numerous carcinoma glands detected.

Healthy glands detected in both longitudnal section and cross-section.

Adipose/Muscle tissue detected under ‚Other tissues‘.

Precised recognition of carcinoma glands.

Exclusion of stroma with unbelievable accuracy.

Digitalisation of slides

HSA KIT is versatile and works well with Leica GT 450 Automatic scanner. However, HSA SCAN M software is an affordable alternative to other scanners. The slides can be manually digitalized directly on your microscope and create manual WSI (Whole Slide Images).