** Bronze Medal Solution for RSNA 2024 Lumbar Spine Degenerative Classification Competition:**
\nhttps://www.kaggle.com/competitions/rsna-2024-lumbar-spine-degenerative-classification/
I would like to express our deepest gratitude to Kaggle and the competition organizers Radiological Society of North America(RSNA) for organizing this very interesting competition. Congratulations to all the prize and medal winners in this competition. I am glad to win the Bronze medal with a rank of 176th.
\nReference to very interesting public solutions and discussion topics posted by participants in this competition.
\nI started with public Ensemble notebook and refined predictions in the RSNA 2024 Lumbar Spine Degenerative Classification competition. The primary emphasis is on ensemble methods and model architecture selection.
\nEnsemble Solutions:
\nThe ensembling is a key technique for improving prediction accuracy. This approach involves combining predictions from multiple models, each potentially having different strengths.
\nModel Architectures
\n I combined various CNN architectures, seeking those best suited for this competition such as :
\n●DenseNet201
\n●EfficientNet_B4
\n●ResNet models
\n●edgenext_base.in21k_ft_in1k
\n● efficientnet_b5.sw_in12k_ft_in1k
Also emphasize the importance of considering the number of parameters and computational resources when selecting a model. For instance, DenseNet201 and EfficientNet_B4 have similar parameter counts and can be handled by Kaggle GPUs. Also, the parameters for various MobileNet, Vision Transformer, Xception, and EfficientNet models and consider the computation demands.
\nOther Factors Influencing Accuracy:
\nThe other factors that can affect prediction accuracy:
\n● Class Imbalance: The class imbalance as a potential issue, requiring techniques like class weighting or data augmentation to address it.
\n●Overfitting: I recommend techniques like dropout, early stopping, and cross-validation to mitigate the risk of overfitting
\n●Data Diversity: We dont need to stress the importance of using diverse test sets to ensure the model's generalizability and robustness in real-world scenarios
\n●Additional Features: We incorporate features like patient demographics and clinical history which potentially improve diagnostic accuracy
Also used different weights for postprocessing for 3 different targets :
\nNormal_mild: 0.40, 0.60
\nModerate : 0.41, 0.59
\n Severe: 0.42, 0.61
Along with this, I also reduced the precision to 4 decimal places by float_format='%.4f'. The resulting outcome was public LB score of 0.47 and Private LB score of 0.52.
\nConclusion
\nI very much focus on ensemble methods and model architecture selection as key refinement techniques. While ensembling and the computational considerations for choosing different architectures need to be considered. While other factors like class imbalance, overfitting, and data diversity are bread and butter, the fine tuning and post processing play an important role here. Surprising was the role of precision in improving the Public-LB score.
** Bronze Medal Solution for RSNA 2024 Lumbar Spine Degenerative Classification Competition:**
\nhttps://www.kaggle.com/competitions/rsna-2024-lumbar-spine-degenerative-classification/
I would like to express our deepest gratitude to Kaggle and the competition organizers Radiological Society of North America(RSNA) for organizing this very interesting competition. Congratulations to all the prize and medal winners in this competition. I am glad to win the Bronze medal with a rank of 176th.
\nReference to very interesting public solutions and discussion topics posted by participants in this competition.
\nI started with public Ensemble notebook and refined predictions in the RSNA 2024 Lumbar Spine Degenerative Classification competition. The primary emphasis is on ensemble methods and model architecture selection.
\nEnsemble Solutions:
\nThe ensembling is a key technique for improving prediction accuracy. This approach involves combining predictions from multiple models, each potentially having different strengths.
\nModel Architectures
\n I combined various CNN architectures, seeking those best suited for this competition such as :
\n●DenseNet201
\n●EfficientNet_B4
\n●ResNet models
\n●edgenext_base.in21k_ft_in1k
\n● efficientnet_b5.sw_in12k_ft_in1k
Also emphasize the importance of considering the number of parameters and computational resources when selecting a model. For instance, DenseNet201 and EfficientNet_B4 have similar parameter counts and can be handled by Kaggle GPUs. Also, the parameters for various MobileNet, Vision Transformer, Xception, and EfficientNet models and consider the computation demands.
\nOther Factors Influencing Accuracy:
\nThe other factors that can affect prediction accuracy:
\n● Class Imbalance: The class imbalance as a potential issue, requiring techniques like class weighting or data augmentation to address it.
\n●Overfitting: I recommend techniques like dropout, early stopping, and cross-validation to mitigate the risk of overfitting
\n●Data Diversity: We dont need to stress the importance of using diverse test sets to ensure the model's generalizability and robustness in real-world scenarios
\n●Additional Features: We incorporate features like patient demographics and clinical history which potentially improve diagnostic accuracy
Also used different weights for postprocessing for 3 different targets :
\nNormal_mild: 0.40, 0.60
\nModerate : 0.41, 0.59
\n Severe: 0.42, 0.61
Along with this, I also reduced the precision to 4 decimal places by float_format='%.4f'. The resulting outcome was public LB score of 0.47 and Private LB score of 0.52.
\nConclusion
\nI very much focus on ensemble methods and model architecture selection as key refinement techniques. While ensembling and the computational considerations for choosing different architectures need to be considered. While other factors like class imbalance, overfitting, and data diversity are bread and butter, the fine tuning and post processing play an important role here. Surprising was the role of precision in improving the Public-LB score.