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I worked on a project to analyze endoscope videos to find diseases. I examined a lot of images and videos annotated with symptoms of various diseases labeled by

I worked on a project to analyze endoscope videos to find diseases. I examined a lot of images and videos annotated with symptoms of various diseases labeled by assistants and doctors. Most of them are really obvious, but others are almost impossible to detect. In rare cases, despite my best efforts, I couldn't see any difference between the spot labeled as a symptom of cancer and the surrounding area. There's no a-ha moment, like finding an insect mimicking its environment. No matter how many times I tried, I just couldn't see any difference.

aswegs8 2y ago
Mind sharing how to get a foot into the field? I've got a good amount of domain knowledge from my studies in life science and rather meager experience from learning to code on my own for a few years. It seems like I cant compete with CS majors and gotta find a way to leverage my domain knowledge.
- hamasho 2y ago
  I'm not an expert in machine learning, but rather a web developer and data engineer helping develop a system to detect diseases from endoscopy images using the model developed by other ML engineers. And it was 5 years ago when I worked on the project, so please take it with a grain of salt. If you want to learn machine learning for healthcare in general, it may help to start problems with tabular data like CSVs instead of images. Image processing is a lot harder, and takes a lot of time and computational power. But it's best to learn what you're interested in the most. Anyway, first you need to be familiar with basic; Python, machine learning, and popular libraries like scikit-learn, matplotlib, numpy, and pandas. Those are tons of articles, textbooks, and videos to help you learn them. If you grasp the basics, I think it's better to learn from actual code to train/evaluate models rather than more theories. Kaggle may be a good starting point. They host a lot of competitions for machine learning problems. There are easy competitions for beginners, and competitions and datasets in the medical field. You can view notebooks (actual code to solve those problems well written by experts) and popular ones are very educational. You can learn a lot by reading those code, understanding concepts and how to use libraries, and modifying some code to see how it changes the result. ChatGPT is also helpful. If you want to learn image classification, the technology used to detect objects from images and videos is called image classification and object detection. It uses CNN, one of the deep neural networks. You also need to learn basic image processing, how to train a deep neural network, how to evaluate, and libraries like OpenCV/Pillow/PyTorch/TorchVision. There are a lot of image classification competitions in the medical field on Kaggle too[0][1]. To run those notebooks, I recommend Google Colab. Image processing often uses a lot of GPUs, and you may not have GPUs, or even if you have it's difficult to set up the right environment. It's easier to use those dedicated cloud services and it doesn't cost much. It's hard to learn, but sometimes fun, so enjoy your journey! [0] https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia https://www.kaggle.com/datasets/paultimothymooney/chest-xray... [1] https://www.kaggle.com/code/arkapravagupta/endoscopy-multiclassification-explained https://www.kaggle.com/code/arkapravagupta/endoscopy-multicl...

aswegs8 2y ago

Mind sharing how to get a foot into the field? I've got a good amount of domain knowledge from my studies in life science and rather meager experience from learning to code on my own for a few years. It seems like I cant compete with CS majors and gotta find a way to leverage my domain knowledge.

hamasho 2y ago
I'm not an expert in machine learning, but rather a web developer and data engineer helping develop a system to detect diseases from endoscopy images using the model developed by other ML engineers. And it was 5 years ago when I worked on the project, so please take it with a grain of salt. If you want to learn machine learning for healthcare in general, it may help to start problems with tabular data like CSVs instead of images. Image processing is a lot harder, and takes a lot of time and computational power. But it's best to learn what you're interested in the most. Anyway, first you need to be familiar with basic; Python, machine learning, and popular libraries like scikit-learn, matplotlib, numpy, and pandas. Those are tons of articles, textbooks, and videos to help you learn them. If you grasp the basics, I think it's better to learn from actual code to train/evaluate models rather than more theories. Kaggle may be a good starting point. They host a lot of competitions for machine learning problems. There are easy competitions for beginners, and competitions and datasets in the medical field. You can view notebooks (actual code to solve those problems well written by experts) and popular ones are very educational. You can learn a lot by reading those code, understanding concepts and how to use libraries, and modifying some code to see how it changes the result. ChatGPT is also helpful. If you want to learn image classification, the technology used to detect objects from images and videos is called image classification and object detection. It uses CNN, one of the deep neural networks. You also need to learn basic image processing, how to train a deep neural network, how to evaluate, and libraries like OpenCV/Pillow/PyTorch/TorchVision. There are a lot of image classification competitions in the medical field on Kaggle too[0][1]. To run those notebooks, I recommend Google Colab. Image processing often uses a lot of GPUs, and you may not have GPUs, or even if you have it's difficult to set up the right environment. It's easier to use those dedicated cloud services and it doesn't cost much. It's hard to learn, but sometimes fun, so enjoy your journey! [0] https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia https://www.kaggle.com/datasets/paultimothymooney/chest-xray... [1] https://www.kaggle.com/code/arkapravagupta/endoscopy-multiclassification-explained https://www.kaggle.com/code/arkapravagupta/endoscopy-multicl...