\n
![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F8639347%2Fa9a303d2e4e84445e1afe831b2566a26%2FScreenshot%20from%202024-09-25%2012-14-32.png?generation=1727262900473330&alt=media\")
I'm working on a multi-task deep learning model aimed at solving a lumbar spine analysis problem, where the tasks involve predicting coordinates of spinal features, levels, and severities. I'd like to share two approaches I've been considering and would appreciate your thoughts and feedback.
\nIn this approach, we divide the prediction process into several tasks, each contributing to the overall analysis of lumbar spine conditions. The key idea is to predict coordinates and their uncertainties first, followed by predicting the spinal level, and then crop around these coordinates to train a severity prediction task.
\n
![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F8639347%2F9791b702f423fb758de5261b0f79ca61%2FScreenshot%20from%202024-09-25%2012-16-12.png?generation=1727263007995134&alt=media\")
Advantages:
\nDisadvantages:
\nIn this second approach, the model predicts all tasks (coordinates, level, and severity) simultaneously in a single pass. Here, the tasks share the backbone of the network, and the network learns to jointly optimize all the predictions.
\n![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F8639347%2Fa67e4a68c0d5f04430314ea9f3e43b23%2FScreenshot%20from%202024-09-25%2012-16-31.png?generation=1727263029716645&alt=media\")
Advantages:
\nDisadvantages:
\nBoth approaches offer unique benefits. The stepwise approach allows for more interpretability and modularity, while the end-to-end approach is computationally efficient and can potentially leverage shared information across tasks. I'm curious to hear your thoughts on these approaches! Have any of you tried something similar, or do you have suggestions for improvement?
", "messages": [ { "id": "2998208", "postDate": "09/25/2024 11:18:44", "content": "I'm working on a multi-task deep learning model aimed at solving a lumbar spine analysis problem, where the tasks involve predicting coordinates of spinal features, levels, and severities. I'd like to share two approaches I've been considering and would appreciate your thoughts and feedback.
\nIn this approach, we divide the prediction process into several tasks, each contributing to the overall analysis of lumbar spine conditions. The key idea is to predict coordinates and their uncertainties first, followed by predicting the spinal level, and then crop around these coordinates to train a severity prediction task.
\n
Advantages:
\nDisadvantages:
\nIn this second approach, the model predicts all tasks (coordinates, level, and severity) simultaneously in a single pass. Here, the tasks share the backbone of the network, and the network learns to jointly optimize all the predictions.
\n
Advantages:
\nDisadvantages:
\nBoth approaches offer unique benefits. The stepwise approach allows for more interpretability and modularity, while the end-to-end approach is computationally efficient and can potentially leverage shared information across tasks. I'm curious to hear your thoughts on these approaches! Have any of you tried something similar, or do you have suggestions for improvement?
", "rawMarkdown": "## Multi-task Learning for Lumbar Spine Analysis: Two Approaches\n\n### Introduction\n\nI'm working on a multi-task deep learning model aimed at solving a lumbar spine analysis problem, where the tasks involve predicting coordinates of spinal features, levels, and severities. I'd like to share two approaches I've been considering and would appreciate your thoughts and feedback.\n\n### **Approach 1: Stepwise Multi-task Prediction**\n\nIn this approach, we divide the prediction process into several tasks, each contributing to the overall analysis of lumbar spine conditions. The key idea is to predict coordinates and their uncertainties first, followed by predicting the spinal level, and then crop around these coordinates to train a severity prediction task.\n\n\n\n\n\n\n**Advantages:**\n- **Interpretability**: This approach provides a stepwise understanding of how each prediction is made.\n- **Modularity**: Each task can be fine-tuned independently.\n\n**Disadvantages:**\n- **Error Propagation**: Mistakes made in coordinate prediction can negatively impact the level and severity predictions.\n- **Computational Overhead**: Multiple stages may slow down inference time.\n\n### **Approach 2: End-to-End Multi-task Prediction**\n\nIn this second approach, the model predicts all tasks (coordinates, level, and severity) simultaneously in a single pass. Here, the tasks share the backbone of the network, and the network learns to jointly optimize all the predictions.\n\n\n\n**Advantages:**\n- **Efficiency**: The model predicts everything in one pass, making it faster at inference.\n- **Joint Optimization**: The model can leverage shared features to improve performance across tasks.\n\n**Disadvantages:**\n- **Less Interpretability**: It can be harder to understand how the model arrived at specific predictions.\n- **Learning Complexity**: Simultaneously learning multiple tasks may be more challenging for the model, especially if one task dominates.\n\n### Conclusion\n\nBoth approaches offer unique benefits. The stepwise approach allows for more interpretability and modularity, while the end-to-end approach is computationally efficient and can potentially leverage shared information across tasks. I'm curious to hear your thoughts on these approaches! Have any of you tried something similar, or do you have suggestions for improvement?", "votes": null }, { "id": "3000710", "postDate": "09/28/2024 01:23:52", "content": "can we just make this question a img-classficiant rather than an img_objection? without using x or y?
", "rawMarkdown": "can we just make this question a img-classficiant rather than an img_objection? without using x or y?", "votes": null }, { "id": "3000850", "postDate": "09/28/2024 06:42:08", "content": "I think img-classficiant method is hard to grab all feature's. You can sum all features (last layer before global pooling ) channel's and resize them and overlay on the image and you may find the bright region doesn't
\n cover all we wanted region.
I try to train a model, such as YOLO, to get ROI of each image, and then train anoather model to classify these ROI's level and severity.But I find that the classification model didn't perform well.I guess it's because the roi are small and simple so it can not give too much feature information.
", "rawMarkdown": "I try to train a model, such as YOLO, to get ROI of each image, and then train anoather model to classify these ROI's level and severity.But I find that the classification model didn't perform well.I guess it's because the roi are small and simple so it can not give too much feature information.", "votes": null }, { "id": "3001002", "postDate": "09/28/2024 10:43:06", "content": "先生谢谢您的回答捏，这样的话我是不是可以先用一个图像分割模型将我们感兴趣的区域给分割出来，然后对分割出来的图片再进行图像分类
", "rawMarkdown": "先生谢谢您的回答捏，这样的话我是不是可以先用一个图像分割模型将我们感兴趣的区域给分割出来，然后对分割出来的图片再进行图像分类", "votes": null }, { "id": "3001550", "postDate": "09/29/2024 01:20:11", "content": "可以试试，但是这种方法有可能由于上下文信息不足，导致分类效果较差，而且正如topic author所说，会有误差传播的影响。我还是偏向于one stage 分类网络，但是要去做一下特征引导
", "rawMarkdown": "可以试试，但是这种方法有可能由于上下文信息不足，导致分类效果较差，而且正如topic author所说，会有误差传播的影响。我还是偏向于one stage 分类网络，但是要去做一下特征引导", "votes": null }, { "id": "3001552", "postDate": "09/29/2024 01:25:23", "content": "Maybe Segmentation is not precise,you can debug with cropped gt rois to valid your method
", "rawMarkdown": "Maybe Segmentation is not precise,you can debug with cropped gt rois to valid your method", "votes": null }, { "id": "3001559", "postDate": "09/29/2024 01:45:49", "content": "先生可以具体说说特征引导的方法咩。然后有兴趣一起组个队咩，想要和你学习学习捏
", "rawMarkdown": "先生可以具体说说特征引导的方法咩。然后有兴趣一起组个队咩，想要和你学习学习捏", "votes": null }, { "id": "3001576", "postDate": "09/29/2024 02:43:27", "content": "组队来不及了，你可以试试把coordinate用起来，让网络去关注一下coordinate标注的区域，而不是让它去学习它感兴趣的区域
", "rawMarkdown": "组队来不及了，你可以试试把coordinate用起来，让网络去关注一下coordinate标注的区域，而不是让它去学习它感兴趣的区域", "votes": null }, { "id": "3001603", "postDate": "09/29/2024 03:37:46", "content": "没事，方便到时私下留个联系方式咩，主要想和您学习学习，因为我也才刚入门。
\n我现在在对T1的数据进行了分割取出图像感兴趣的地方然后进行数据增强看看有没有效果
\n因为其他两类的数据分割还不太会写，先写了一个看看效果
不了不了，我很菜，只会纸上谈兵哈哈哈，而且我暂时想专注one stage
", "rawMarkdown": "不了不了，我很菜，只会纸上谈兵哈哈哈，而且我暂时想专注one stage", "votes": null }, { "id": "3001629", "postDate": "09/29/2024 04:44:38", "content": "大佬谦虚了。下次有机会一起组队捏
", "rawMarkdown": "大佬谦虚了。下次有机会一起组队捏", "votes": null }, { "id": "3007096", "postDate": "10/05/2024 00:20:06", "content": "I think the key to raising the cap on the \"two-stage plan\" lies in the design of the second-stage plan.
", "rawMarkdown": "I think the key to raising the cap on the \"two-stage plan\" lies in the design of the second-stage plan.", "votes": null }, { "id": "3008633", "postDate": "10/06/2024 21:30:42", "content": "Your 1st approach has two problems.
\nProblem 1:
\nIf you look at data carefully, you will see some Images have multiple x,y coordinates, while some images only have one x,y coordinate, this suggests that there can be multiple locations or points of interest in the same image.
\nExample:
\nStudy Id 7143189 => Series Id 1951927562 => 11.dcm has two x and y coordinates in the same Axial T2 View while others have only one x and y coordinates.
Problem 2:
\nEven if you detect x,y coordinates and then level, predicting the severity condition after that doesn't make any sense since any level can have any severity condition.
Well, I'm no expert, I can be 100% wrong here, so suggestions are appreciated.
", "rawMarkdown": "Your 1st approach has two problems.\n\nProblem 1:\nIf you look at data carefully, you will see some Images have multiple x,y coordinates, while some images only have one x,y coordinate, this suggests that there can be multiple locations or points of interest in the same image. \nExample:\nStudy Id 7143189 => Series Id 1951927562 => 11.dcm has two x and y coordinates in the same Axial T2 View while others have only one x and y coordinates.\n\nProblem 2:\nEven if you detect x,y coordinates and then level, predicting the severity condition after that doesn't make any sense since any level can have any severity condition.\n\nWell, I'm no expert, I can be 100% wrong here, so suggestions are appreciated.", "votes": null } ], "comments": [ { "id": 3000710, "author_name": "ottolumous", "author_url": "", "post_date": "09/28/2024 01:23:52", "content": "can we just make this question a img-classficiant rather than an img_objection? without using x or y?
", "votes": null, "replies": [ { "id": 3000850, "author_name": "xukongji", "author_url": "", "post_date": "09/28/2024 06:42:08", "content": "I think img-classficiant method is hard to grab all feature's. You can sum all features (last layer before global pooling ) channel's and resize them and overlay on the image and you may find the bright region doesn't
\n cover all we wanted region.
先生谢谢您的回答捏，这样的话我是不是可以先用一个图像分割模型将我们感兴趣的区域给分割出来，然后对分割出来的图片再进行图像分类
", "votes": null, "replies": [ { "id": 3001550, "author_name": "xukongji", "author_url": "", "post_date": "09/29/2024 01:20:11", "content": "可以试试，但是这种方法有可能由于上下文信息不足，导致分类效果较差，而且正如topic author所说，会有误差传播的影响。我还是偏向于one stage 分类网络，但是要去做一下特征引导
", "votes": null, "replies": [ { "id": 3001559, "author_name": "ottolumous", "author_url": "", "post_date": "09/29/2024 01:45:49", "content": "先生可以具体说说特征引导的方法咩。然后有兴趣一起组个队咩，想要和你学习学习捏
", "votes": null, "replies": [ { "id": 3001576, "author_name": "xukongji", "author_url": "", "post_date": "09/29/2024 02:43:27", "content": "组队来不及了，你可以试试把coordinate用起来，让网络去关注一下coordinate标注的区域，而不是让它去学习它感兴趣的区域
", "votes": null, "replies": [ { "id": 3001603, "author_name": "ottolumous", "author_url": "", "post_date": "09/29/2024 03:37:46", "content": "没事，方便到时私下留个联系方式咩，主要想和您学习学习，因为我也才刚入门。
\n我现在在对T1的数据进行了分割取出图像感兴趣的地方然后进行数据增强看看有没有效果
\n因为其他两类的数据分割还不太会写，先写了一个看看效果
不了不了，我很菜，只会纸上谈兵哈哈哈，而且我暂时想专注one stage
", "votes": null, "replies": [ { "id": 3001629, "author_name": "ottolumous", "author_url": "", "post_date": "09/29/2024 04:44:38", "content": "大佬谦虚了。下次有机会一起组队捏
", "votes": null, "replies": [] } ] } ] } ] } ] } ] } ] } ] } ] }, { "id": 3000924, "author_name": "i2nfinit3y", "author_url": "", "post_date": "09/28/2024 09:01:25", "content": "I try to train a model, such as YOLO, to get ROI of each image, and then train anoather model to classify these ROI's level and severity.But I find that the classification model didn't perform well.I guess it's because the roi are small and simple so it can not give too much feature information.
", "votes": null, "replies": [ { "id": 3001552, "author_name": "xukongji", "author_url": "", "post_date": "09/29/2024 01:25:23", "content": "Maybe Segmentation is not precise,you can debug with cropped gt rois to valid your method
", "votes": null, "replies": [] } ] }, { "id": 3007096, "author_name": "wangziming77", "author_url": "", "post_date": "10/05/2024 00:20:06", "content": "I think the key to raising the cap on the \"two-stage plan\" lies in the design of the second-stage plan.
", "votes": null, "replies": [] }, { "id": 3008633, "author_name": "tashijawed", "author_url": "", "post_date": "10/06/2024 21:30:42", "content": "Your 1st approach has two problems.
\nProblem 1:
\nIf you look at data carefully, you will see some Images have multiple x,y coordinates, while some images only have one x,y coordinate, this suggests that there can be multiple locations or points of interest in the same image.
\nExample:
\nStudy Id 7143189 => Series Id 1951927562 => 11.dcm has two x and y coordinates in the same Axial T2 View while others have only one x and y coordinates.
Problem 2:
\nEven if you detect x,y coordinates and then level, predicting the severity condition after that doesn't make any sense since any level can have any severity condition.
Well, I'm no expert, I can be 100% wrong here, so suggestions are appreciated.
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "2998208": "## Multi-task Learning for Lumbar Spine Analysis: Two Approaches\n\n### Introduction\n\nI'm working on a multi-task deep learning model aimed at solving a lumbar spine analysis problem, where the tasks involve predicting coordinates of spinal features, levels, and severities. I'd like to share two approaches I've been considering and would appreciate your thoughts and feedback.\n\n### **Approach 1: Stepwise Multi-task Prediction**\n\nIn this approach, we divide the prediction process into several tasks, each contributing to the overall analysis of lumbar spine conditions. The key idea is to predict coordinates and their uncertainties first, followed by predicting the spinal level, and then crop around these coordinates to train a severity prediction task.\n\n\n\n\n\n\n**Advantages:**\n- **Interpretability**: This approach provides a stepwise understanding of how each prediction is made.\n- **Modularity**: Each task can be fine-tuned independently.\n\n**Disadvantages:**\n- **Error Propagation**: Mistakes made in coordinate prediction can negatively impact the level and severity predictions.\n- **Computational Overhead**: Multiple stages may slow down inference time.\n\n### **Approach 2: End-to-End Multi-task Prediction**\n\nIn this second approach, the model predicts all tasks (coordinates, level, and severity) simultaneously in a single pass. Here, the tasks share the backbone of the network, and the network learns to jointly optimize all the predictions.\n\n\n\n**Advantages:**\n- **Efficiency**: The model predicts everything in one pass, making it faster at inference.\n- **Joint Optimization**: The model can leverage shared features to improve performance across tasks.\n\n**Disadvantages:**\n- **Less Interpretability**: It can be harder to understand how the model arrived at specific predictions.\n- **Learning Complexity**: Simultaneously learning multiple tasks may be more challenging for the model, especially if one task dominates.\n\n### Conclusion\n\nBoth approaches offer unique benefits. The stepwise approach allows for more interpretability and modularity, while the end-to-end approach is computationally efficient and can potentially leverage shared information across tasks. I'm curious to hear your thoughts on these approaches! Have any of you tried something similar, or do you have suggestions for improvement?", "3000710": "can we just make this question a img-classficiant rather than an img_objection? without using x or y?", "3000850": "I think img-classficiant method is hard to grab all feature's. You can sum all features (last layer before global pooling ) channel's and resize them and overlay on the image and you may find the bright region doesn't \n cover all we wanted region.", "3000924": "I try to train a model, such as YOLO, to get ROI of each image, and then train anoather model to classify these ROI's level and severity.But I find that the classification model didn't perform well.I guess it's because the roi are small and simple so it can not give too much feature information.", "3001002": "先生谢谢您的回答捏，这样的话我是不是可以先用一个图像分割模型将我们感兴趣的区域给分割出来，然后对分割出来的图片再进行图像分类", "3001550": "可以试试，但是这种方法有可能由于上下文信息不足，导致分类效果较差，而且正如topic author所说，会有误差传播的影响。我还是偏向于one stage 分类网络，但是要去做一下特征引导", "3001552": "Maybe Segmentation is not precise,you can debug with cropped gt rois to valid your method", "3001559": "先生可以具体说说特征引导的方法咩。然后有兴趣一起组个队咩，想要和你学习学习捏", "3001576": "组队来不及了，你可以试试把coordinate用起来，让网络去关注一下coordinate标注的区域，而不是让它去学习它感兴趣的区域", "3001603": "没事，方便到时私下留个联系方式咩，主要想和您学习学习，因为我也才刚入门。\n我现在在对T1的数据进行了分割取出图像感兴趣的地方然后进行数据增强看看有没有效果\n因为其他两类的数据分割还不太会写，先写了一个看看效果", "3001623": "不了不了，我很菜，只会纸上谈兵哈哈哈，而且我暂时想专注one stage", "3001629": "大佬谦虚了。下次有机会一起组队捏", "3007096": "I think the key to raising the cap on the \"two-stage plan\" lies in the design of the second-stage plan.", "3008633": "Your 1st approach has two problems.\n\nProblem 1:\nIf you look at data carefully, you will see some Images have multiple x,y coordinates, while some images only have one x,y coordinate, this suggests that there can be multiple locations or points of interest in the same image. \nExample:\nStudy Id 7143189 => Series Id 1951927562 => 11.dcm has two x and y coordinates in the same Axial T2 View while others have only one x and y coordinates.\n\nProblem 2:\nEven if you detect x,y coordinates and then level, predicting the severity condition after that doesn't make any sense since any level can have any severity condition.\n\nWell, I'm no expert, I can be 100% wrong here, so suggestions are appreciated." }, "source": "meta" }