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@@ -392,7 +392,7 @@ Typically, model training is a time-consuming step during deep learning developm
 NVIDIA GPUs have been widely applied in many areas of deep learning training and evaluation, and the CUDA parallel computation shows obvious acceleration when comparing to traditional computation methods. To fully leverage GPU features, many popular mechanisms raised, like automatic mixed precision (AMP), distributed data parallel, etc. MONAI can support these features and provides rich examples.
 
 ### 1. Profiling the pipelines
-First of all, MONAI provides several methods based on `DLProf`, `Nsight`, `NVTX` and `NVML` for users to analyze their programs to identify the performance bottleneck. The analyses include operation-based GPU activity and overall GPU activity during model training. They will greatly help users manage computing bottlenecks and provide insights for the area to be improved for better computing efficiency. The detailed example is shown in the [performance profiling tutorial]( https://github.com/Project-MONAI/tutorials/blob/master/performance_profiling/profiling_train_base_nvtx.ipynb).
+First of all, MONAI provides several methods based on `DLProf`, `Nsight`, `NVTX` and `NVML` for users to analyze their programs to identify the performance bottleneck. The analyses include operation-based GPU activity and overall GPU activity during model training. They will greatly help users manage computing bottlenecks and provide insights for the area to be improved for better computing efficiency. The detailed example is shown in the [performance profiling tutorial](https://github.com/Project-MONAI/tutorials/blob/master/performance_profiling/radiology/profiling_train_base_nvtx.md).
 
 ### 2. Auto mixed precision(AMP)
 In 2017, NVIDIA researchers developed a methodology for mixed-precision training, which combined single-precision (FP32) with half-precision (e.g. FP16) format when training a network, and it achieved the same accuracy as FP32 training using the same hyperparameters.