Update README.md

README.md

@@ -68,36 +68,29 @@ This dataset cannot be utilized for segmentation task since no ground truth imag
 ### Curation Rationale
 
 <!-- Motivation for the creation of this dataset. -->
-Kidney diseases can be challenging to detect, while identification or diagnosis is crucial for treatment. 
-This dataset is intended to develop AI and machine learning algorithms for enhanced kidney disease recognition.
+Kidney diseases often present significant detection challenges, yet their timely identification and diagnosis are critical for effective treatment.
+
+The Kidney-CT-Abnormality dataset is curated with the express purpose of facilitating the development of advanced artificial intelligence (AI) and machine learning algorithms aimed at enhancing the recognition and diagnostic accuracy of kidney diseases.
 
 
 ### Source Data
 
 <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-https://zenodo.org/records/8043408
 
 #### Data Collection and Processing
 
 <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-https://zenodo.org/records/8014290
+This dataset's original homepage: https://zenodo.org/records/8043408
+The dataset was adapted from https://zenodo.org/records/8014290 as mentioned before.
 The original dataset contains “215 thoraxabdomen CT scans with segmentations of the kidney and abnormalities in the kidney”. Note that the original datasets contains in total 38.4G image data in mha format, and there is no .json file indicating the abnormality status. Alternatively, a segmentation kidney image dataset is included.
 
 
-#### Who are the source data producers?
-
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
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 ### Annotations 
 
 <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
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 <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
-
 <!-- This section describes the people or systems who created the annotations. -->
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+The annotation information, which are the abnormality labels, are completed by the original authors and included the json file.
 
 
 #### Personal and Sensitive Information
@@ -111,7 +104,8 @@ The CT scan image file names contain the study ID, indicating the differences in
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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+The dataset only include abnormality label, with no further implication of specific diseases. This can limit the algorithms diagnostic specificity.
+Moreover, the collected data can have potential bias. For instance, the CT scans might be generated from specific demographics, which can introduce bias (skewing the representation and applicability of the data).
 
 ### Recommendations