Running Model on Custom Dataset - Request for Review and Feedback

[anantpal07]

Dear seungjunlee,

I hope this message finds you well. I am writing to inform you that we have successfully run your model on our custom dataset. Excitedly, we have obtained some preliminary results and would greatly appreciate your expertise in reviewing them.

In an effort to contribute to the advancement of the model's performance and utility, we have meticulously prepared and conducted experiments with our dataset. We believe that sharing our findings with you could help refine the model's capabilities and potentially uncover areas for improvement.

To facilitate this collaboration, we have attached the results of our experiments for your review. We kindly request your insightful feedback on the following aspects:

Accuracy: Are our results in line with your expectations for this model? If not, could you please guide us on potential reasons for discrepancies and how we might enhance accuracy?

Performance Metrics: Have we appropriately evaluated the model's performance metrics? Are there additional metrics we should consider to provide a comprehensive assessment?

Data Preprocessing: Could any improvements be made to our data preprocessing methods to better align with the model's requirements and optimize performance?

Model Configuration: Are there specific adjustments or fine-tuning techniques we should consider to enhance the model's compatibility with our dataset?

General Recommendations: Based on our findings, do you have any general recommendations or insights that could aid us in further refining our experiments and achieving better results?

Your expertise and guidance are invaluable to us, and we eagerly await your feedback. Please feel free to reach out to us with any questions or clarifications you may have regarding our experiments or the attached results.

Thank you very much for considering our findings and for your continued dedication to advancing the field.

Warm regards,

Anant Paliwal paliwalanant07@gmail.com study ID_aaf114858f patient ID_5f3cc378 subarachnoid.zip

[seungjunlee96]

Dear Anant Paliwal,

Thank you for sharing your experiments and results. I've reviewed the data and would like to offer some feedback to enhance our collaborative efforts:

1. Data Format: It appears there's an issue with how the CT slice images are organized. For optimal performance, the model relies on these images being sorted correctly, as it assesses adjacent parts for anomaly detection. Please ensure the images are sorted by their names or another consistent metric.

2. Reconstruction Quality: The reconstruction quality of the images seems to be significantly lower than expected. This discrepancy could be due to various factors, including preprocessing techniques (the aforementioned data format issues) or model parameter settings. I recommend revisiting these aspects to align closer with the expected quality.

3. Brain Extraction: The results of the brain extraction process do not seem accurate. This could be a consequence of the aforementioned issues or might indicate a need for adjustment in the specific algorithm or parameters used for brain extraction.

To address these points, I suggest reviewing and correcting the data format issue first, as it likely impacts the subsequent stages. Additionally, please consider revisiting your preprocessing steps and model configuration to ensure they are optimized for your dataset.

I look forward to seeing the adjustments and discussing further improvements. Your efforts are highly valued, and I'm confident that with these modifications, we'll achieve better results.

Best regards,

Seungjun Lee

[anantpal07]

Dear Seungjunlee,

Thank you for your valuable feedback and recommendations. I greatly appreciate your insights and have taken them into consideration while refining our experiments.

Here are the adjustments we've made based on your suggestions:

Data Format: We have addressed the issue with the organization of CT slice images. We ensured that the images are now sorted correctly by their names, which is crucial for the model's performance in assessing adjacent parts for anomaly detection.

Reconstruction Quality: To improve the reconstruction quality of the images, we revisited our preprocessing techniques and model parameter settings. Specifically, we focused on aligning the preprocessing steps more closely with the expected quality standards. Additionally, we optimized the model configuration to enhance the reconstruction output.

Brain Extraction: We thoroughly reviewed the results of the brain extraction process and identified areas for improvement. We adjusted the algorithm and parameters used for brain extraction to achieve more accurate results. By refining this step, we aim to enhance the overall performance of the model in detecting anomalies.

Furthermore, I'm pleased to inform you that we have successfully converted our DICOM files into NIfTI format and generated masks using the CT BET model on our dataset. Subsequently, we converted the masks into NumPy arrays and the DICOM files into PNG format, ensuring consistency in dimensions and HU levels.

In terms of computational resources, we utilized an Nvidia RTX A100 GPU with 80GB of RAM, 16 vCPUs, and Ubuntu 22.04 as the operating system. These high-performance resources significantly expedited our experiments and enabled us to achieve more efficient results. Brain_CT_Triage_Study.zip

With these adjustments and refinements in place, we reran the triage model using pretrained checkpoints and obtained improved results. We are eager to share these results with you and discuss any further insights or recommendations you may have.

Once again, thank you for your guidance and support throughout this collaborative effort. I look forward to our continued collaboration and the opportunity to further enhance the performance of our model.

Best regards,

Anant Paliwal

[anantpal07]

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