Fix typos in self_supervised_pretraining README

self_supervised_pretraining/README.md

@@ -18,7 +18,7 @@ to use for fine-tuning tasks and directly skip to the second part of the tutoria
 3.) Run the 'ssl_script_train.py'\
 4.) Modify the paths for data_root, json_path, pre-trained_weights_path from 2.) and
 logdir_path in 'ssl_finetuning_train.py'\
-5.) Run the ssl_finetuning_script.py\
+5.) Run the 'ssl_finetuning_script.py'\
 6.) And that's all folks, use the model to your needs
 
 ### 1.Data
@@ -69,8 +69,8 @@ arXiv preprint arXiv:2103.10504 (2021).
 ### 3. Self-supervised Tasks
 
 The pre-training pipeline has two aspects to it (Refer figure shown below). First, it uses augmentation (top row) to
-mutate the data and the second is it utilizes to a regularized
-[constrastive loss](https://docs.monai.io/en/latest/losses.html#contrastiveloss) [3] to learn feature representations
+mutate the data and second, it utilizes regularized
+[contrastive loss](https://docs.monai.io/en/latest/losses.html#contrastiveloss) [3] to learn feature representations
 of the unlabeled data. The multiple augmentations are applied on a randomly selected 3D foreground patch from a 3D
 volume. Two augmented views of the same 3D patch are generated for the contrastive loss as it functions by drawing
 the two augmented views closer to each other if the views are generated from the same patch, if not then it tries to
@@ -81,7 +81,7 @@ maximize the disagreement. The CL offers this functionality on a mini-batch.
 The augmentations mutate the 3D patch in various ways, the primary task of the network is to reconstruct
 the original image. The different augmentations used are classical techniques such as in-painting [1], out-painting [1]
 and noise augmentation to the image by local pixel shuffling [2]. The secondary task of the network is to simultaneously
-reconstruct the two augmented views as similar to each other as possible via regularized contrastive loss [3] as it's
+reconstruct the two augmented views as similar to each other as possible via regularized contrastive loss [3] as its
 objective is to maximize the agreement. The term regularized has been used here because contrastive loss is adjusted
 by the reconstruction loss as a dynamic weight itself.
 
@@ -90,7 +90,7 @@ The below example image depicts the usage of the augmentation pipeline where two
 
 ![image](../figures/SSL_Different_Augviews.png)
 
-Multiple axial slice of a 96x96x96 patch are shown before the augmentation (Ref Original Patch in the above figure).
+Multiple axial slices of a 96x96x96 patch are shown before the augmentation (Ref Original Patch in the above figure).
 Augmented View 1 & 2 are different augmentations generated via the transforms on the same cubic patch. The objective
 of the SSL network is to reconstruct the original top row image from the first view. The contrastive loss
 is driven by maximizing agreement of the reconstruction based on input of the two augmented views.