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README.md

@@ -11,7 +11,8 @@ size_categories:
 - 10K<n<100K
 ---
 
-# PathVQA
+# Dataset Card for PathVQA
+![image](image.png)
 
 ## Dataset Description
 PathVQA is a dataset of question-answer pairs on pathology images. The dataset is intended to be used for training and testing 
@@ -20,7 +21,7 @@ Board of Pathology (ABP) test. The dataset includes both open-ended questions an
 built from two publicly-available pathology textbooks: "Textbook of Pathology" and "Basic Pathology", and a publicly-available 
 digital library: "Pathology Education Informational Resource" (PEIR). The copyrights of images and captions belong to the 
 publishers and authors of these two books, and the owners of the PEIR digital library.<br> 
-![image](image.png)<br>
+
 **Repository:** [PathVQA Official GitHub Repository](https://github.com/UCSD-AI4H/PathVQA)<br>
 **Paper:** [PathVQA: 30000+ Questions for Medical Visual Question Answering](https://arxiv.org/abs/2003.10286)<br>
 **Leaderboard:** [Papers with Code Leaderboard](https://paperswithcode.com/sota/medical-visual-question-answering-on-pathvqa)
@@ -47,33 +48,15 @@ The question-answer pairs are in English.
 
 ### Data Instances
 Each instance consists of an image-question-answer triplet.
-
-Example from training dataset:
-```
-{
-  'image': 'train_0001',
-  'question': 'What is the appearance of the chromatin texture,  with fine and coarse clumps?',
-  'answer': 'a salt-and-pepper pattern'
-}
-```
-Example from validation dataset:
 ```
 {
-  'image': 'val_0000',
-  'question': 'How are the organisms?',
-  'answer': 'somewhat variable in size'
-}
-```
-Example from test dataset:
-```
-{
-  'image': 'test_0001',
-  'question': 'Is squamous cell carcinoma composed of nests of malignant cells that partially recapitulate the stratified organization of squamous epithelium?',
-  'answer': 'yes'
+  'image': {'bytes': b'\xff\xd8\xff\xee\x00\x0eAdobe\x00d..., 'path': None},
+  'question': 'What does immunoperoxidase staining reveal that marks positively with anti-CD4 antibodies?',
+  'answer': 'a predominantly perivascular cellular infiltrate'
 }
 ```
 ### Data Fields
-- `'image'`: the filename of the referenced image, excluding the file extension, which is `.jpg`. 
+- `'image'`: the image referenced by the question-answer pair, as a byte array. 
 - `'question'`: the text of the question about the image.
 - `'answer'`: the text of the expected answer.
 
@@ -83,11 +66,9 @@ The dataset is split into training, validation and test. The split is provided d
 ## Additional Information
 
 ### Licensing Information
-
 The authors have released the dataset under the [MIT License](https://github.com/UCSD-AI4H/PathVQA/blob/master/LICENSE).
 
 ### Citation Information
-
 ```
 @article{he2020pathvqa,
     title={PathVQA: 30000+ Questions for Medical Visual Question Answering},

path_vqa.py

@@ -1,8 +1,7 @@
 """PathVQA: 30000+ Questions for Medical Visual Question Answering"""
 
-import pandas
 import os
-
+import pandas
 import datasets
 
 _CITATION = """\
@@ -15,11 +14,13 @@ _CITATION = """\
 """
 
 _DESCRIPTION = """\
-    PathVQA is a dataset of question-answer pairs on pathology images. The questions are similar to those in the
-    American Board of Pathology (ABP) test. The dataset includes both open-ended questions and binary "yes/no"
-    questions. The dataset is built from two publicly-available pathology textbooks: "Textbook of Pathology" and
-    "Basic Pathology", and a publicly-available digital library: "Pathology Education Informational Resource"
-    (PEIR). The copyrights of images and captions belong to the publishers and authors of these two books,
+    PathVQA is a dataset of question-answer pairs on pathology images. The dataset is intended to
+    be used for training and testing Medical Visual Question Answering (VQA) systems. The questions
+    contained in the dataset are similar to those in the American Board of Pathology (ABP) test. The
+    dataset includes both open-ended questions and binary "yes/no" questions. The dataset is built
+    from two publicly-available pathology textbooks: "Textbook of Pathology" and "Basic Pathology",
+    and a publicly-available digital library: "Pathology Education Informational Resource" (PEIR).
+    The copyrights of images and captions belong to the publishers and authors of these two books,
     and the owners of the PEIR digital library.
 """
 
@@ -28,12 +29,9 @@ _HOMEPAGE = "https://github.com/UCSD-AI4H/PathVQA"
 _LICENSE = "MIT"
 
 _URLS = {
-    "image_train": "data/image/train_img.tar",
-    "image_val": "data/image/val_img.tar",
-    "image_test": "data/image/test_img.tar",
-    "text_train": "data/text/train_qa.jsonl",
-    "text_val": "data/text/val_qa.jsonl",
-    "text_test": "data/text/test_qa.jsonl",
+    "train": "data/train.parquet",
+    "val": "data/val.parquet",
+    "test": "data/test.parquet",
 }
 
 class PathVQA(datasets.GeneratorBasedBuilder):
@@ -41,95 +39,62 @@ class PathVQA(datasets.GeneratorBasedBuilder):
     """
     PathVQA: 30000+ Questions for Medical Visual Question Answering.
     
-    The data was obtained from the updated Google Drive link shared by the authors in their GitHub repository
-    on Feb 15, 2023, see https://github.com/UCSD-AI4H/PathVQA/commit/117e7f4ef88a0e65b0e7f37b98a73d6237a3ceab.
-    
-    This version of the dataset contains a total of 5,004 images and 32,795 question-answer pairs. Of the
-    5,004 images, 4,289 images are referenced by a question-answer pair, while 715 images are not used.
-    Furthermore, there are several duplicates, i.e. there are some image-question-answer triplets which occur
-    more than once in the same split (train, val, test). After dropping the duplicate image-question-answer
+    The data was obtained from the updated Google Drive link shared by the authors on Feb 15, 2023,
+    see https://github.com/UCSD-AI4H/PathVQA/commit/117e7f4ef88a0e65b0e7f37b98a73d6237a3ceab. This
+    version of the dataset contains a total of 5,004 images and 32,795 question-answer pairs. Out
+    of the 5,004 images, 4,289 images are referenced by a question-answer pair, while 715 images
+    are not used. There are a few image-question-answer triplets which occur more than once in the
+    same split (training, validation, test). After dropping the duplicate image-question-answer
     triplets, the dataset contains 32,632 question-answer pairs on 4,289 images.
     """
 
     VERSION = datasets.Version("0.1.0")
     
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="full", version=VERSION, description="Original dataset."),
-        datasets.BuilderConfig(name="de-duped", version=VERSION, description="De-duplicated dataset."),
-    ]
-
-    DEFAULT_CONFIG_NAME = "de-duped"
-    
     def _info(self):
-        
-        features = datasets.Features(
-            {
-                "image": datasets.Image(),
-                "question": datasets.Value("string"),
-                "answer": datasets.Value("string")
-            }
-        )
-        
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
-            features=features,
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "question": datasets.Value("string"),
+                    "answer": datasets.Value("string")
+                }
+            ),
             homepage=_HOMEPAGE,
             license=_LICENSE,
             citation=_CITATION,
         )
 
     def _split_generators(self, dl_manager):
-        
-        # images
-        image_train_dir = dl_manager.download_and_extract(_URLS["image_train"])
-        image_val_dir = dl_manager.download_and_extract(_URLS["image_val"])
-        image_test_dir = dl_manager.download_and_extract(_URLS["image_test"])
-
-        # question-answer pairs
-        text_train_dir = dl_manager.download(_URLS["text_train"])
-        text_val_dir = dl_manager.download(_URLS["text_val"])
-        text_test_dir = dl_manager.download(_URLS["text_test"])
-
         return [
-            
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "image_filepath": os.path.join(image_train_dir),
-                    "text_filepath": os.path.join(text_train_dir),
+                    "filepath": os.path.join(dl_manager.download(_URLS["train"])),
                     "split": "train",
                 },
             ),
-            
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 gen_kwargs={
-                    "image_filepath": os.path.join(image_val_dir),
-                    "text_filepath": os.path.join(text_val_dir),
+                    "filepath": os.path.join(dl_manager.download(_URLS["val"])),
                     "split": "val",
                 },
             ),
-            
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "image_filepath": os.path.join(image_test_dir),
-                    "text_filepath": os.path.join(text_test_dir),
+                    "filepath": os.path.join(dl_manager.download(_URLS["test"])),
                     "split": "test"
                 },
             ),
         ]
 
-    def _generate_examples(self, image_filepath, text_filepath, split):
-        
-        df = pandas.read_json(text_filepath, orient='records', lines=True)
-        if self.config.name == "de-duped":
-            df = df.drop_duplicates(ignore_index=True)
-        
+    def _generate_examples(self, filepath, split):
+        df = pandas.read_parquet(filepath)
         for key, row in df.iterrows():
             yield key, {
-                "image": os.path.join(image_filepath, row['image']),
+                "image": row["image"],
                 "question": row["question"],
                 "answer": row["answer"]
             }
-