ترجمه فارسی مقاله تشخیص نمونه های کم ارائه شده با استفاده از آموزش دسته ای پویا برای تقسیم بندی تومور مغز از تصاویر MR

• کتاب صدها نکته فارسی (خودمونی) – نسخه PDF — زبان ساده و کاربردی
  مشاهده نمونه نسخه نکات ساده
• کتاب صدها نکته رسمی فارسی – نسخه PDF — نگارش استاندارد و علمی
  مشاهده نمونه نسخه نکات رسمی
• کتاب صدها پرسش و پاسخ تشریحی – نسخه PDF
  — هر سؤال همراه با پاسخ کامل برای درک عمیق مفاهیم
  مشاهده نمونه نسخه پرسش و پاسخ
• کتاب پرسش و پاسخ چهارگزینه‌ای – نسخه یادگیری سریع
  — پاسخ‌ها بلافاصله بعد از سؤال برای مرور سریع
  مشاهده نمونه نسخه کوییز سریع
• کتاب پرسش و پاسخ چهارگزینه‌ای – نسخه خودآزمایی
  — پاسخ‌ها در انتهای بخش‌ها برای سنجش واقعی یادگیری
  مشاهده نمونه نسخه آزمونی

🎯 این بسته یک دورهٔ آموزشی کامل و چندلایه است؛ شامل ویدیوهای آموزشی، کتاب‌ها، تمرین‌ها و خودآزمایی.

چکیده

Brain tumors in magnetic resonance imaging (MR) are difficult, time-consuming, and prone to human error. These challenges can be resolved by developing automatic brain tumor segmentation methods from MR images. Various deep-learning models based on the U-Net have been proposed for the task. These deep-learning models are trained on a dataset of tumor images and then used for segmenting the masks. Mini-batch training is a widely used method in deep learning for training. However, one of the significant challenges associated with this approach is that if the training dataset has under-represented samples or samples with complex latent representations, the model may not generalize well to these samples. The issue leads to skewed learning of the data, where the model learns to fit towards the majority representations while underestimating the under-represented samples. The proposed dynamic batch training method addresses the challenges posed by under-represented data points, data points with complex latent representation, and imbalances within the class, where some samples may be harder to learn than others. Poor performance of such samples can be identified only after the completion of the training, leading to the wastage of computational resources. Also, training easy samples after each epoch is an inefficient utilization of computation resources. To overcome these challenges, the proposed method identifies hard samples and trains such samples for more iterations compared to easier samples on the BraTS2020 dataset. Additionally, the samples trained multiple times are identified and it provides a way to identify hard samples in the BraTS2020 dataset. The comparison of the proposed training approach with U-Net and other models in the literature highlights the capabilities of the proposed training approach.

چکیده به فارسی (ترجمه ماشینی)