![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F6425763%2Fa0fb5286e43e320f7254305ceaece32e%2F3.png?generation=1728561152061914&alt=media\")
First of all, I would like to extend my thanks to the organizers of this competition. It was my first time participating in a Kaggle challenge, as well as my first experience working with medical data, and I absolutely loved it.
\nFor this challenge, I utilized the RSNA Dataset exclusively, without any modifications. My approach consisted of three main stages, which were somewhat similar to other solutions. Here's an overview of my method:
I would especially appreciate feedback on my experimental ensembling approach in Step 3, as it’s an area I’m keen to improve.
\nIn the first stage, I focused on identifying the \"best\" slice from the MRI series for each vertebra level. This involved several key steps:
\n(batch_size, n_levels = 5, n_slices) to align with the five vertebrae levels.
In the second stage, I focused on estimating the positions of the vertebrae in both sagittal and axial scans, in order to extract relevant crops. The process differed slightly depending on the scan type:
\n![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F6425763%2F9c97cbff5f50aef43a8db6643e8048da%2F4.png?generation=1728561213635803&alt=media\")
In this final stage, I experimented with an ensembling method implemented in a single training process. The key aspects of my approach were as follows:
\nInput: The model received crops derived from the 5 best consecutive slices, each cropped to a size of 128x128 pixels.
Model Architecture: I created a model consisting of N1 pretrained encoders (utilizing different architectures) and N2 classifiers.
Random Selection During Training: For each training batch, I randomly selected one encoder and one classifier to connect and generate a prediction.
\n![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F6425763%2Fc19ea522ba678a5002b6f86aa6741480%2F1.png?generation=1728560469076623&alt=media\")
Inference Process: During inference, I passed the outputs of all encoders through every classifier and averaged the results to obtain the final prediction.
\n![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F6425763%2F21002622388d729eec9a21f10bd91620%2F2.png?generation=1728560502557952&alt=media\")
Objective: The goal was to generalize the encoders to produce different feature vectors that lead to coherent predictions, all of which can be understood by the classifiers. I ensured that backpropagation was never performed on the five encoders; otherwise, the model would overfit very quickly.
There are still many improvements to be made in this approach, but based on my tests (albeit biased since I really liked the idea) this method yielded better results than training N separate models and averaging their predictions afterward.
", "messages": [ { "id": 3013670, "postDate": "2024-10-10T11:58:47.687Z", "content": "First of all, I would like to extend my thanks to the organizers of this competition. It was my first time participating in a Kaggle challenge, as well as my first experience working with medical data, and I absolutely loved it.
\nFor this challenge, I utilized the RSNA Dataset exclusively, without any modifications. My approach consisted of three main stages, which were somewhat similar to other solutions. Here's an overview of my method:
I would especially appreciate feedback on my experimental ensembling approach in Step 3, as it’s an area I’m keen to improve.
\nIn the first stage, I focused on identifying the \"best\" slice from the MRI series for each vertebra level. This involved several key steps:
\n(batch_size, n_levels = 5, n_slices) to align with the five vertebrae levels.
In the second stage, I focused on estimating the positions of the vertebrae in both sagittal and axial scans, in order to extract relevant crops. The process differed slightly depending on the scan type:
\n
In this final stage, I experimented with an ensembling method implemented in a single training process. The key aspects of my approach were as follows:
\nInput: The model received crops derived from the 5 best consecutive slices, each cropped to a size of 128x128 pixels.
Model Architecture: I created a model consisting of N1 pretrained encoders (utilizing different architectures) and N2 classifiers.
Random Selection During Training: For each training batch, I randomly selected one encoder and one classifier to connect and generate a prediction.
\n
Inference Process: During inference, I passed the outputs of all encoders through every classifier and averaged the results to obtain the final prediction.
\n
Objective: The goal was to generalize the encoders to produce different feature vectors that lead to coherent predictions, all of which can be understood by the classifiers. I ensured that backpropagation was never performed on the five encoders; otherwise, the model would overfit very quickly.
There are still many improvements to be made in this approach, but based on my tests (albeit biased since I really liked the idea) this method yielded better results than training N separate models and averaging their predictions afterward.
", "rawMarkdown": "First of all, I would like to extend my thanks to the organizers of this competition. It was my first time participating in a Kaggle challenge, as well as my first experience working with medical data, and I absolutely loved it.\nFor this challenge, I utilized the RSNA Dataset exclusively, without any modifications. My approach consisted of three main stages, which were somewhat similar to other solutions. Here's an overview of my method:\n- **Stage 1**: Detection of the \"correct\" slices in the MRI series for each vertebra.\n- **Stage 2**: Estimation of the vertebrae positions to extract the corresponding crops.\n- **Stage 3**: Experimental approach to ensembling. I used an ensemble model, implemented in a single training process, for the classification of the severity of degenerative spine conditions.\n\nI would especially appreciate feedback on my experimental ensembling approach in Step 3, as it’s an area I’m keen to improve.\n\n## **Stage 1: Detecting the Best Slice for Each Vertebra Level**\n\nIn the first stage, I focused on identifying the \"best\" slice from the MRI series for each vertebra level. This involved several key steps:\n\n- Dataset Utilization: I used the dataset to extract the slice that provided the clearest view of each vertebra level.\n- Heatmap Generation: For each vertebra level, I generated a heatmap to help identify the optimal slice.\n- Model Architecture: I implemented a model composed of an encoder, a transformer layer, and a classifier to predict the position of the \"best\" slice for each vertebra level.\n- Output Reshaping: The model’s output was reshaped into the format `(batch_size, n_levels = 5, n_slices)` to align with the five vertebrae levels.\n- Training: I used gradient accumulation during training to avoid the need for padding\n\n\n\n## **Stage 2: Estimating Vertebrae Positions for Cropping**\n\nIn the second stage, I focused on estimating the positions of the vertebrae in both sagittal and axial scans, in order to extract relevant crops. The process differed slightly depending on the scan type:\n\n#### For Sagittal Series:\n- Best Slices Selection: From the results of Step 1, I selected the 3 best consecutive slices overall by summing all vertebra levels and picking the top 3. I then put the slices in RGB channels (for a 2.5D model)\n- Segmentation Model: I used a segmentation model to estimate the vertebra positions and applied a center of mass method to extract precise coordinates.\n- Dual-Head Model: The model also had a second head that directly predicted the x, y coordinates. Although less accurate than segmentation, this head was used as a fallback when the segmentation head returned no activations.\n- Output: The model returned 5 channels and 5 corresponding positions—one for each vertebra level.\n#### For Axial Series:\n- Best Slices per Level: Similar to sagittal scans, but in this case, the model received the 3 best slices for each vertebra level and returned the position specifically for that level.\n- Same Approach: The segmentation and coordinate prediction process followed the same approach as for the sagittal scans.\n\n\n\n## **Stage 3: Classification of Severity**\nIn this final stage, I experimented with an ensembling method implemented in a single training process. The key aspects of my approach were as follows:\n- Input: The model received crops derived from the 5 best consecutive slices, each cropped to a size of 128x128 pixels.\n- Model Architecture: I created a model consisting of N1 pretrained encoders (utilizing different architectures) and N2 classifiers.\n- Random Selection During Training: For each training batch, I randomly selected one encoder and one classifier to connect and generate a prediction.\n\n\n- Inference Process: During inference, I passed the outputs of all encoders through every classifier and averaged the results to obtain the final prediction.\n\n\n- Objective: The goal was to generalize the encoders to produce different feature vectors that lead to coherent predictions, all of which can be understood by the classifiers. I ensured that backpropagation was never performed on the five encoders; otherwise, the model would overfit very quickly.\n\n\nThere are still many improvements to be made in this approach, but based on my tests (albeit biased since I really liked the idea) this method yielded better results than training N separate models and averaging their predictions afterward.\n\n\n\n", "votes": 7 } ], "comments": [], "raw_markdown_by_id": { "3013670": "First of all, I would like to extend my thanks to the organizers of this competition. It was my first time participating in a Kaggle challenge, as well as my first experience working with medical data, and I absolutely loved it.\nFor this challenge, I utilized the RSNA Dataset exclusively, without any modifications. My approach consisted of three main stages, which were somewhat similar to other solutions. Here's an overview of my method:\n- **Stage 1**: Detection of the \"correct\" slices in the MRI series for each vertebra.\n- **Stage 2**: Estimation of the vertebrae positions to extract the corresponding crops.\n- **Stage 3**: Experimental approach to ensembling. I used an ensemble model, implemented in a single training process, for the classification of the severity of degenerative spine conditions.\n\nI would especially appreciate feedback on my experimental ensembling approach in Step 3, as it’s an area I’m keen to improve.\n\n## **Stage 1: Detecting the Best Slice for Each Vertebra Level**\n\nIn the first stage, I focused on identifying the \"best\" slice from the MRI series for each vertebra level. This involved several key steps:\n\n- Dataset Utilization: I used the dataset to extract the slice that provided the clearest view of each vertebra level.\n- Heatmap Generation: For each vertebra level, I generated a heatmap to help identify the optimal slice.\n- Model Architecture: I implemented a model composed of an encoder, a transformer layer, and a classifier to predict the position of the \"best\" slice for each vertebra level.\n- Output Reshaping: The model’s output was reshaped into the format `(batch_size, n_levels = 5, n_slices)` to align with the five vertebrae levels.\n- Training: I used gradient accumulation during training to avoid the need for padding\n\n\n\n## **Stage 2: Estimating Vertebrae Positions for Cropping**\n\nIn the second stage, I focused on estimating the positions of the vertebrae in both sagittal and axial scans, in order to extract relevant crops. The process differed slightly depending on the scan type:\n\n#### For Sagittal Series:\n- Best Slices Selection: From the results of Step 1, I selected the 3 best consecutive slices overall by summing all vertebra levels and picking the top 3. I then put the slices in RGB channels (for a 2.5D model)\n- Segmentation Model: I used a segmentation model to estimate the vertebra positions and applied a center of mass method to extract precise coordinates.\n- Dual-Head Model: The model also had a second head that directly predicted the x, y coordinates. Although less accurate than segmentation, this head was used as a fallback when the segmentation head returned no activations.\n- Output: The model returned 5 channels and 5 corresponding positions—one for each vertebra level.\n#### For Axial Series:\n- Best Slices per Level: Similar to sagittal scans, but in this case, the model received the 3 best slices for each vertebra level and returned the position specifically for that level.\n- Same Approach: The segmentation and coordinate prediction process followed the same approach as for the sagittal scans.\n\n\n\n## **Stage 3: Classification of Severity**\nIn this final stage, I experimented with an ensembling method implemented in a single training process. The key aspects of my approach were as follows:\n- Input: The model received crops derived from the 5 best consecutive slices, each cropped to a size of 128x128 pixels.\n- Model Architecture: I created a model consisting of N1 pretrained encoders (utilizing different architectures) and N2 classifiers.\n- Random Selection During Training: For each training batch, I randomly selected one encoder and one classifier to connect and generate a prediction.\n\n\n- Inference Process: During inference, I passed the outputs of all encoders through every classifier and averaged the results to obtain the final prediction.\n\n\n- Objective: The goal was to generalize the encoders to produce different feature vectors that lead to coherent predictions, all of which can be understood by the classifiers. I ensured that backpropagation was never performed on the five encoders; otherwise, the model would overfit very quickly.\n\n\nThere are still many improvements to be made in this approach, but based on my tests (albeit biased since I really liked the idea) this method yielded better results than training N separate models and averaging their predictions afterward.\n\n\n\n" } }