Please, let me know the apps, platforms, or methodologies based on AI to analyze images, such as radiographic or histology images. Tell me your experience of using AI in assessing patterns, segmentation, and other tasks which could be easy to use, and preferable with a low cost.
Various AI-based tools exist for analyzing medical images like radiographs and histology slides. Open-source options such as ImageJ/Fiji and MONAI offer powerful capabilities at no cost but may require more technical expertise. Cloud-based solutions like Google Cloud Healthcare API and IBM Watson Health Imaging provide user-friendly interfaces with scalable pricing. Specialized platforms like PathAI focus on specific areas like histology. The choice depends on your specific needs, image types, technical expertise, and budget. Popular options include MONAI, NIH's CBIIT Image Tools, Arterys, Enlitic, and Definiens. For ease of use and low cost, open-source tools or cloud-based solutions with flexible pricing models might be good starting points, but it's advisable to trial different options to find the best fit for your particular use case.
To analyze images such as radiographic or histology images, popular AI tools for creating models include TensorFlow, PyTorch, and Keras, while OpenCV is used for real-time image processing. Amazon Rekognition and Google Cloud Vision AI offer pre-built models and are easily scalable. MATLAB is suitable for advanced analysis but can be costlier.
For cost-effective computational power, platforms like Kaggle, Azure Machine Learning, and Google Colab provide limited free access to powerful GPUs. In my experience, Keras is excellent for simple model creation, OpenCV is effective for preprocessing, and cloud services efficiently handle large datasets. These tools are easy to use and affordable, making them suitable for a variety of image analysis tasks.
In general, researchers use Python code for development and deployment can be handled by tools such as Tensorflow Lite. On the web, specifically in websites such as paperswithcode, kaggle and huggingface, you can find what's the state of the art for each different tasks. There's currently research ongoing on the use of multimodal Large Language Models (LLMs) to assist medical diagnosis and analysis based on imagery from different sources (not sure about the reliability and interpretability of these systems right now). In general, you can easily find models for even highly specific tasks (e.g. analysis involving a specific organ or disease). One thing to watch for is interpretability. Current research on medical images is highly based on neural networks which are in general hard to interpret (they will output results but it is hard for you to know why). Good luck on your search and hopes this helps.
My list of free goodies:
- PyTorch (https://pytorch.org/) and Tensorflow (https://www.tensorflow.org/): For model development and inference
- Tensorflow Lite (https://www.tensorflow.org/lite): For lightweight inference
- OpenCV (https://opencv.org/): For image processing
- pydicom (https://pypi.org/project/pydicom/): To work with DICOM files
- Scikit-learn (https://scikit-learn.org/): Specially for model evaluation. Also see scikit-image for some image analysis tools
- NumPy (https://numpy.org/): For numerical computation and linear algebra when needed. Also useful for simple array operation as they're highly optimized for this library.
- Goggle Colab (https://colab.research.google.com/): For free GPU and CPU usage and cool notebook environment with lots of pre-setup tools
- Kaggle (https://kaggle.com/): 30 hours of free GPU weekly, competitions using real data (sometimes organized by conferences and accredited insititutions), free datasets and leaderboards, great community with lots of tips, some free courses too.
- PapersWithCode (https://paperswithcode.com/): Keep up with research AND code for all kinds of tasks (including highly specific ones). Also, presents datasets and has leaderboards for them (for even different tasks.
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