Update README.md

README.md

@@ -32,6 +32,8 @@ tasks in medical imaging, especially focusing on the detection and understanding
 ### Dataset Sources
 
 <!-- Provide the basic links for the dataset. -->
+The source data for the "FracAtlas" dataset is hosted on Figshare, an online digital repository where researchers can preserve and share their research outputs, including datasets. The FracAtlas dataset is freely accessible under a CC-BY 4.0 license, allowing for widespread use in the scientific community, particularly among researchers and practitioners in medical imaging and related fields.
+The data had created, cleaned, and managed by Iftekharul Abedeen, Md. Ashiqur Rahman, Fatema Zohra Prottyasha, Tasnim Ahmed, Tareque Mohmud Chowdhury & Swakkhar Shatabda. More details related to Data Collection & Annotation can be seen in ###Source Data section.
 
 - **Repository:** https://figshare.com/articles/dataset/The_dataset/22363012
 - **Paper:** https://www.nature.com/articles/s41597-023-02432-4
@@ -80,23 +82,22 @@ The full schema of the HuggingFace dataset loader is below:
 - **Image [image]:** A PIL image object denoting each X-ray image. This can be used to load the image file directly. <br />
 - **hand[ClassLabel]:** A binary indicators (1 or 0) marking the presence of hand in the radiograph <br />
 - **leg [ClassLabel]:** A binary indicators (1 or 0) marking the presence of leg in the radiograph <br />
-- **hip [int]:** A binary indicators (1 or 0) marking the presence of hip in the radiograph <br />
-- **shoulder [int]:** A binary indicator (1 or 0) marking the shoulder in the radiograph <br />
-- **mixed [int]:** A binary indicator of whether the image contains multiple body parts <br />
-- **hardware [int]:** A binary indicator marking the presence of medical hardware (i.e. screws or plates) in the image <br />
-- **multiscan [int]:** A binary indicator signifies whether the image is part of a set of multiple scans <br />
-- **fractured [int]:** A binary indicator of whether there is a fracture present in the image <br />
+- **hip [ClassLabel]:** A binary indicators (1 or 0) marking the presence of hip in the radiograph <br />
+- **shoulder [ClassLabel]:** A binary indicator (1 or 0) marking the shoulder in the radiograph <br />
+- **mixed [ClassLabel]:** A binary indicator of whether the image contains multiple body parts <br />
+- **hardware [ClassLabel]:** A binary indicator marking the presence of medical hardware (i.e. screws or plates) in the image <br />
+- **multiscan [ClassLabel]:** A binary indicator signifies whether the image is part of a set of multiple scans <br />
+- **fractured [ClassLabel]:** A binary indicator of whether there is a fracture present in the image <br />
 - **fracture_count [int]:** The number of fractures present in the image <br />
+- **frontal [ClassLabel]:** A binary indicator (1 or 0) denoting the front orientation of the radiograph <br />
+- **lateral[ClassLabel]:**  A binary indicator (1 or 0) denoting the side orientation of the radiograph <br />
+- **oblique [ClassLabel]:** A binary indicator (1 or 0) denoting denoting the angled orientation of the radiograph <br />
+- **localization_metadata [dict/Features]:** Metadata about the image localization, including 1) width(int), height (int), and depth (int) of the image
+- **segmentation_metadata[dict/Features]:** Metadata about the segmentation, including the 1) segmentation(Sequence of Sequence of floats), 2) bounding box(Sequence of floats), and 3) area(float) covered by the segmentation. This can be None if no segmentation data is available
+
+Also, we should note that even though the authors claim that annotations are provided only for images with fractures, it is worth-noting that some of the non-fracture images also have annotation data, and some of the fracture images do not. Therefore, to maximize the integrity of the data, both **Fractured** and **Segmentation_metadata** are kept for users. That is probably because annotations are done manlually and thus subject to errors, as the authors mentioned in the corresponding paper.
+Furthermore, **hand**, **leg**, **hip**, and **shoulder** are not mutually exclusive, so they are stored as independent variables. 
 
-    - Image_path [string]: A string representing the file path for its corresponding image \
-    - segmentation [list]: A list object contains the segmentation info for each fractured image \
-    - bbox [list]: A list object contains the bounding box info for each fractured image \
-    - area [float]: A float denoting the area of the bounding box in each fractured image 
-    - height [int] \
-    - width [int] \
-    - Depth [int]
-    - segmented [int] \
-    
 ### Curation Rationale
 
 <!-- Motivation for the creation of this dataset. -->
@@ -115,7 +116,7 @@ All the X-ray scans that have been collected are for general-purpose diagnosis.
 This means along with bone fracture scans there are also samples for chest diseases and abnormalities in the skull and spine region. 
 In the collected data the number of bone fracture samples in the chest, skull and spine region was sparse. 
 As a result, scans for the said parts were removed with the supervision of a medical officer. 
-This left us with 4,083 scans from the hand, leg, hip and shoulder regions. Figure 2 shows some valid vs outlier images for the dataset. 
+This left us with 4,083 scans from the hand, leg, hip and shoulder regions.
 
 #### Data Collection and Processing
 
@@ -151,13 +152,8 @@ All personally identifiable information in the gathered data has been removed, a
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-While the FracAtlas dataset is particularly valuable for the development of computer-aided diagnosis systems, its potential limitations should be carefully considered. Firstly, the manual annotation process, is susceptible to human error, which may result in mislabeling. Such inaccuracies can impact the performance of machine learning models trained on this data. 
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
+While the FracAtlas dataset is particularly valuable for the development of computer-aided diagnosis systems, its potential limitations should be carefully considered. Firstly, the manual annotation process, is susceptible to human error, which may result in mislabeling. Such inaccuracies can impact the performance of machine learning models trained on this data. For example, the authors claim that annotations (segmentation, area, bounding box) are provided only for fracture images, some non-fractured images also have annotations. Conversely, some fracturd images miss corresponding annotations.  
+It should be noted that to use the dataset correctly, one needs to have knowledge of medical and radiology fields to understand the results and make conclusions based on the dataset. It's also important to consider the possibility of labeling errors.
 
 ## Citation