Add audited original VQA subset with source labels, natural sentence targets and image-grouped splits

README.md

@@ -1,101 +1,126 @@
----
-language:
-- en
-license: other
-license_name: source-specific-noncommercial-research-terms
-license_link: https://huggingface.co/datasets/Grayye233/BN4601A-Colonoscopy-Curated/blob/main/licenses/SOURCE_TERMS.md
-task_categories:
-- image-segmentation
-- visual-question-answering
-tags:
-- colonoscopy
-- polyp
-- academic-research
-- weak-labels
-configs:
-- config_name: segmentation
-  default: true
-  data_files:
-  - split: train
-    path: segmentation/train/*.parquet
-  - split: validation
-    path: segmentation/val/*.parquet
-  - split: test
-    path: segmentation/test/*.parquet
-- config_name: core_sft
-  data_files:
-  - split: train
-    path: core_sft/train.parquet
-  - split: validation
-    path: core_sft/val.parquet
----
-
-# BN4601A curated colonoscopy dataset
-
-An academic curation of Kvasir-SEG and PolypGen, with audited identities, fixed center splits, native binary masks, mask geometry and linked weak-label Examine / Explain / Report examples. This repository is hosted by Grayye233 for the BN4601A project. It does not claim ownership of the original images or create a new independently collected clinical cohort.
-
-**Public repository:** teammates can view and download it without sharing the owner's credentials. All data remain subject to their source-specific research and education terms. This is not a clinical diagnostic dataset with histology labels. Read [source terms](licenses/SOURCE_TERMS.md) before use.
-
-## See what the data look like
-
-![Nine illustrative source images with annotated-mask overlays](preview.png)
-
-The orange regions are source annotation masks, not model predictions. These examples illustrate train/validation/test and positive/negative inputs; they do not summarize model accuracy. Source credits: Jha et al. for Kvasir-SEG; Ali et al. for PolypGen. Full selected IDs: `preview_samples.json`.
-
-Use the Dataset Viewer to select `segmentation` or `core_sft` and a split. Segmentation rows show image and mask columns plus source, center, group, positivity, original dimensions and geometry. In `core_sft`, `image_id` links to the segmentation row; image bytes are stored once rather than repeated in every task.
-
-## Included configurations
-
-| Configuration | Train | Validation | Test | Contents |
-|---|---:|---:|---:|---|
-| segmentation | 5,373 | 1,287 | 2,375 | 9,035 source images, native binary masks and metadata |
-| core_sft | 6,987 | 1,062 | None | 8,049 weak task records linked to 2,329 train and 354 validation images |
-
-The original project audited 9,649 source frames, removed two exact decoded-image duplicates, and used 9,647 distinct frames. **612 CVC-ClinicDB frames were external evaluation only and are not included as image/mask bytes here.** Their audited identity manifest is supplied as `external_cvc_reference.csv`. The current author site lists a separate data-use agreement; its complete redistribution conditions could not be verified during release. Obtain CVC through its author channel and comply with applicable terms. This omission does not remove any frame from the project's completed evaluation, and it does not remove any training or validation frame.
-
-| Source | Role in this repository |
-|---|---|
-| Kvasir-SEG | 800 train / 100 validation / 100 test; all positive |
-| PolypGen C1-C4 | 4,573 training frames including negatives |
-| PolypGen C5 | 1,187 validation frames including negatives |
-| PolypGen C6 | 2,275 test frames: 407 positive and 1,868 negative |
-| CVC-ClinicDB | 612 external identities only; never used for training |
-
-## Changes made to the original data
-
-1. Verified that each source image opens, its paired mask has matching dimensions, and center/sequence assignments follow the source publication.
-2. Assigned frame positivity from the annotation mask, including empty masks in nominal positive folders. For author negative-only sequences without annotation files, generated an empty mask and retained this provenance in `mask_origin`.
-3. Removed two exact duplicate decoded RGB images; preserved the exclusions and all source identities.
-4. Fixed the center-held-out split before model training. No sequence crosses train/validation/test. Still-image patient identifiers are unavailable; near-duplicate independence is not established.
-5. Kept image file bytes unchanged. Converted annotation masks to native-size 0/255 binary PNGs using the same >127 threshold as the completed training/evaluation. No offline image resizing, augmentation or new human contour annotation is claimed. Resizing and random augmentation happen in the training loader.
-6. Computed coverage, exclusive-right/bottom bounding boxes, normalized centroids, 3-by-3 image-relative location and connected-component counts. These measure pixels, not millimeters, bowel segment, histology or verified polyp count.
-7. Generated three English core-task records per selected training/validation image. Sequence sampling is capped at 12 uniformly spaced frames. Answers use mask facts and explicitly synthetic clinician context, not expert-written clinical ground truth. Histology remains unavailable. Actual training rotates one task per image per epoch.
-
-`split` in row metadata uses `val`, while the Hugging Face split is named `validation`; they refer to the same fixed validation set.
-
-## Load in Colab
-
-```python
-!pip -q install datasets pillow
-from datasets import load_dataset
-repo = "Grayye233/BN4601A-Colonoscopy-Curated"
-frames = load_dataset(repo, "segmentation", split="train", streaming=True)
-row = next(iter(frames))
-display(row["image"])
-display(row["mask"])
-print({k: row[k] for k in ["id", "source_dataset", "center", "positive", "coverage_pct"]})
-tasks = load_dataset(repo, "core_sft", split="train", streaming=True)
-print(next(iter(tasks))["answer_json"])
-```
-
-For a reproducible rerun, pin the repository revision shown in the project's publication receipt. `04_Browse_Shared_Dataset.ipynb` in the separate Colab handoff supplies browsing and optional local materialization. The dataset repository contains no trained model weights; complete weights remain in that handoff package.
-
-## Limits and licensing
-
-Research and education only. Kvasir retains its original terms. PolypGen's archive includes conflicting BY and BY-NC-SA notices; both originals are preserved, with the stricter archive notice followed for this release. Our curation and derived text contributions follow CC BY-NC-SA 4.0 subject to source terms; this does not relicense source images. No patient identification, clinical deployment, diagnostic accuracy, physical measurement or histopathology claim is supported. Synthetic family history/removal context is for controlled task learning, not real patient information.
-
-## Original source citations
-
-- Jha et al. (2020), *Kvasir-SEG: A Segmented Polyp Dataset*, MMM. [Source and terms](https://datasets.simula.no/kvasir-seg/), [paper](https://arxiv.org/abs/1911.07069).
-- Ali et al. (2023), *A multi-centre polyp detection and segmentation dataset for generalisability assessment*, Scientific Data 10, 75. [Paper](https://doi.org/10.1038/s41597-023-01981-y), [author repository](https://github.com/DebeshJha/PolypGen).
-- Bernal et al. (2015), *WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians*. [Paper](https://doi.org/10.1016/j.compmedimag.2015.02.007), [author database site](https://pages.cvc.uab.es/CVC-Colon/index.php/databases/), [current access agreement](https://pages.cvc.uab.es/ai4polypnet/wp-content/uploads/2024/05/AgreementData_CVC-1.pdf). CVC pixels are not redistributed here.
+---
+language:
+- en
+license: other
+license_name: source-specific-noncommercial-research-terms
+license_link: https://huggingface.co/datasets/Grayye233/BN4601A-Colonoscopy-Curated/blob/main/licenses/SOURCE_TERMS.md
+task_categories:
+- image-segmentation
+- visual-question-answering
+tags:
+- colonoscopy
+- polyp
+- academic-research
+- weak-labels
+configs:
+- config_name: segmentation
+  default: true
+  data_files:
+  - split: train
+    path: segmentation/train/*.parquet
+  - split: validation
+    path: segmentation/val/*.parquet
+  - split: test
+    path: segmentation/test/*.parquet
+- config_name: core_sft
+  data_files:
+  - split: train
+    path: core_sft/train.parquet
+  - split: validation
+    path: core_sft/val.parquet
+- config_name: vqa_sft
+  data_files:
+  - split: train
+    path: vqa_sft/train.parquet
+  - split: validation
+    path: vqa_sft/val.parquet
+  - split: test
+    path: vqa_sft/test.parquet
+---
+
+# BN4601A curated colonoscopy dataset
+
+An academic curation of Kvasir-SEG and PolypGen, with audited identities, fixed center splits, native binary masks, mask geometry and linked weak-label Examine / Explain / Report examples. This repository is hosted by Grayye233 for the BN4601A project. It does not claim ownership of the original images or create a new independently collected clinical cohort.
+
+**Public repository:** teammates can view and download it without sharing the owner's credentials. All data remain subject to their source-specific research and education terms. This is not a clinical diagnostic dataset with histology labels. Read [source terms](licenses/SOURCE_TERMS.md) before use.
+
+## See what the data look like
+
+![Nine illustrative source images with annotated-mask overlays](preview.png)
+
+The orange regions are source annotation masks, not model predictions. These examples illustrate train/validation/test and positive/negative inputs; they do not summarize model accuracy. Source credits: Jha et al. for Kvasir-SEG; Ali et al. for PolypGen. Full selected IDs: `preview_samples.json`.
+
+Use the Dataset Viewer to select `segmentation` or `core_sft` and a split. Segmentation rows show image and mask columns plus source, center, group, positivity, original dimensions and geometry. In `core_sft`, `image_id` links to the segmentation row; image bytes are stored once rather than repeated in every task.
+
+## Included configurations
+
+| Configuration | Train | Validation | Test | Contents |
+|---|---:|---:|---:|---|
+| segmentation | 5,373 | 1,287 | 2,375 | 9,035 source images, native binary masks and metadata |
+| core_sft | 6,987 | 1,062 | None | 8,049 weak task records linked to 2,329 train and 354 validation images |
+
+The original project audited 9,649 source frames, removed two exact decoded-image duplicates, and used 9,647 distinct frames. **612 CVC-ClinicDB frames were external evaluation only and are not included as image/mask bytes here.** Their audited identity manifest is supplied as `external_cvc_reference.csv`. The current author site lists a separate data-use agreement; its complete redistribution conditions could not be verified during release. Obtain CVC through its author channel and comply with applicable terms. This omission does not remove any frame from the project's completed evaluation, and it does not remove any training or validation frame.
+
+| Source | Role in this repository |
+|---|---|
+| Kvasir-SEG | 800 train / 100 validation / 100 test; all positive |
+| PolypGen C1-C4 | 4,573 training frames including negatives |
+| PolypGen C5 | 1,187 validation frames including negatives |
+| PolypGen C6 | 2,275 test frames: 407 positive and 1,868 negative |
+| CVC-ClinicDB | 612 external identities only; never used for training |
+
+## Changes made to the original data
+
+1. Verified that each source image opens, its paired mask has matching dimensions, and center/sequence assignments follow the source publication.
+2. Assigned frame positivity from the annotation mask, including empty masks in nominal positive folders. For author negative-only sequences without annotation files, generated an empty mask and retained this provenance in `mask_origin`.
+3. Removed two exact duplicate decoded RGB images; preserved the exclusions and all source identities.
+4. Fixed the center-held-out split before model training. No sequence crosses train/validation/test. Still-image patient identifiers are unavailable; near-duplicate independence is not established.
+5. Kept image file bytes unchanged. Converted annotation masks to native-size 0/255 binary PNGs using the same >127 threshold as the completed training/evaluation. No offline image resizing, augmentation or new human contour annotation is claimed. Resizing and random augmentation happen in the training loader.
+6. Computed coverage, exclusive-right/bottom bounding boxes, normalized centroids, 3-by-3 image-relative location and connected-component counts. These measure pixels, not millimeters, bowel segment, histology or verified polyp count.
+7. Generated three English core-task records per selected training/validation image. Sequence sampling is capped at 12 uniformly spaced frames. Answers use mask facts and explicitly synthetic clinician context, not expert-written clinical ground truth. Histology remains unavailable. Actual training rotates one task per image per epoch.
+
+`split` in row metadata uses `val`, while the Hugging Face split is named `validation`; they refer to the same fixed validation set.
+
+## Load in Colab
+
+```python
+!pip -q install datasets pillow
+from datasets import load_dataset
+repo = "Grayye233/BN4601A-Colonoscopy-Curated"
+frames = load_dataset(repo, "segmentation", split="train", streaming=True)
+row = next(iter(frames))
+display(row["image"])
+display(row["mask"])
+print({k: row[k] for k in ["id", "source_dataset", "center", "positive", "coverage_pct"]})
+tasks = load_dataset(repo, "core_sft", split="train", streaming=True)
+print(next(iter(tasks))["answer_json"])
+```
+
+For a reproducible rerun, pin the repository revision shown in the project's publication receipt. `04_Browse_Shared_Dataset.ipynb` in the separate Colab handoff supplies browsing and optional local materialization. The dataset repository contains no trained model weights; complete weights remain in that handoff package.
+
+## Limits and licensing
+
+Research and education only. Kvasir retains its original terms. PolypGen's archive includes conflicting BY and BY-NC-SA notices; both originals are preserved, with the stricter archive notice followed for this release. Our curation and derived text contributions follow CC BY-NC-SA 4.0 subject to source terms; this does not relicense source images. No patient identification, clinical deployment, diagnostic accuracy, physical measurement or histopathology claim is supported. Synthetic family history/removal context is for controlled task learning, not real patient information.
+
+## Original source citations
+
+- Jha et al. (2020), *Kvasir-SEG: A Segmented Polyp Dataset*, MMM. [Source and terms](https://datasets.simula.no/kvasir-seg/), [paper](https://arxiv.org/abs/1911.07069).
+- Ali et al. (2023), *A multi-centre polyp detection and segmentation dataset for generalisability assessment*, Scientific Data 10, 75. [Paper](https://doi.org/10.1038/s41597-023-01981-y), [author repository](https://github.com/DebeshJha/PolypGen).
+- Bernal et al. (2015), *WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians*. [Paper](https://doi.org/10.1016/j.compmedimag.2015.02.007), [author database site](https://pages.cvc.uab.es/CVC-Colon/index.php/databases/), [current access agreement](https://pages.cvc.uab.es/ai4polypnet/wp-content/uploads/2024/05/AgreementData_CVC-1.pdf). CVC pixels are not redistributed here.
+
+
+## Version 2: small original VQA extension
+
+Select **vqa_sft** in the viewer to see image/question/source-answer/natural-sentence records. This configuration is separate from unchanged segmentation/core_sft splits.
+
+| Split | Images | QA records |
+|---|---:|---:|
+| train | 640 | 3339 |
+| val | 105 | 523 |
+| test | 105 | 530 |
+
+Seven direct visual tasks: visible polyp/instrument counts, instruments, text/box overlays, and polyp region/color. Colonoscopy-only source categories include Polyps, Instrument and Ulcerative Colitis; the latter contributes direct visual questions, not disease-diagnosis targets. No size, histology, polyp-type or removal-history targets are included. Original short labels are rendered as fixed English sentences; these are not independently authored clinical explanations. Training questions include two handcrafted paraphrases; the third is reserved for evaluation.
+
+Screening compared 2,944 candidate images against all 9,647 existing core frames using 64-bit pHash distance <=6. 245 candidates overlapping core held-out images were excluded from training. 339 of the 640 training images match core training images; the 70 polyp validation/test images reuse their corresponding core held-out split. Thus these records must not be described as 850 new independent clinical cases. Final VQA splits have no cross-split pHash candidates at the chosen threshold. Source patients/centers are unavailable and patient independence is not established. See vqa_sft/audit.json and exclusions.json.
+
+**License:** original VQA source materials retain CC BY-NC 4.0, attribution to Gautam et al. and their source-specific conditions. See licenses/Kvasir-VQA_SOURCE_CARD.md and source terms. No x1 generated answers are included. Source: https://huggingface.co/datasets/SimulaMet-HOST/Kvasir-VQA/tree/26df9125b98cbad664e5b1801aac1f70a02689e2.

licenses/Kvasir-VQA_SOURCE_CARD.md

@@ -0,0 +1,136 @@
+---
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: source
+    dtype: string
+  - name: question
+    dtype: string
+  - name: answer
+    dtype: string
+  - name: img_id
+    dtype: string
+  splits:
+  - name: raw
+    num_bytes: 15176464880.875
+    num_examples: 58849
+  download_size: 1547864596
+  dataset_size: 15176464880.875
+configs:
+- config_name: default
+  data_files:
+  - split: raw
+    path: data/*.parquet
+license: cc-by-nc-4.0
+task_categories:
+- visual-question-answering
+- image-text-to-text
+language:
+- en
+tags:
+- medical
+- kvasir
+---
+
+The Kvasir-VQA dataset is an extended dataset derived from the HyperKvasir and Kvasir-Instrument datasets, augmented with question-and-answer annotations. This dataset is designed to facilitate advanced machine learning tasks in gastrointestinal (GI) diagnostics, including image captioning, Visual Question Answering (VQA) and text-based generation of synthetic medical images
+
+**Homepage**: [https://datasets.simula.no/kvasir-vqa](https://datasets.simula.no/kvasir-vqa)
+
+# Usage
+You can use the Kvasir-VQA dataset directly from HuggingFace Dataset Hub.
+
+🔥 See [Jupyter Notebook Demo](https://huggingface.co/datasets/SimulaMet-HOST/Kvasir-VQA/blob/main/explore_Kvasir-VQA.ipynb). You can open the notebook on Google Colab.
+
+```
+from datasets import load_dataset
+ds = load_dataset("SimulaMet-HOST/Kvasir-VQA")
+```
+
+## Downloading Dataset as an Image foler and CSV Metadata
+
+```
+d_path ="./" #existing folder where you want to save images and metadata.csv
+
+df = ds['raw'].select_columns(['source', 'question', 'answer', 'img_id']).to_pandas()
+df.to_csv(f"{d_path}/metadata.csv", index=False)
+
+import os
+os.makedirs(f"{d_path}/images", exist_ok=True)
+
+for i, row in df.groupby('img_id').nth(0).iterrows(): # for images
+  image = ds['raw'][i]['image'].save(f"{d_path}/images/{row['img_id']}.jpg")
+```
+
+The total image size is around 1.5 GB. The CSV file will have 58,849 rows.
+
+
+### Key Features
+
+- **Total Images**: 6,500 annotated images
+- **Annotations**: Includes question-and-answer pairs for each image
+- **Question Types**: Yes/No, single-choice, multiple-choice, color-related, location-related, numerical count
+- **Applications**: Image captioning, VQA, synthetic medical image generation, object detection, etc
+
+
+## Dataset Details
+
+### Image Categories
+
+The dataset includes images from various GI tract conditions and medical instruments used in GI procedures:
+
+| Image Category      | Number of Samples | Source Dataset      |
+|---------------------|-------------------|---------------------|
+| Normal              | 2500              | HyperKvasir         |
+| Polyps              | 1000              | HyperKvasir         |
+| Esophagitis         | 1000              | HyperKvasir         |
+| Ulcerative Colitis  | 1000              | HyperKvasir         |
+| Instrument          | 1000              | Kvasir-Instrument   |
+| **TOTAL**           | **6500**          |                     |
+
+
+### Annotation Process
+
+Annotations were developed with input from medical professionals and include six types of questions:
+
+- **Yes/No Questions**
+- **Single-Choice Questions**
+- **Multiple-Choice Questions**
+- **Color-Related Questions**
+- **Location-Related Questions**
+- **Numerical Count Questions**
+
+Annotations cover a range of GI aspects, including findings, abnormalities, anatomical landmarks, and medical instruments.
+
+
+When using the Kvasir-VQA dataset, you should include the following information to ensure compliance with the dataset's usage terms, particularly when citing the dataset in documents or papers:
+
+## Terms of Use
+
+The data is released fully open for research and educational purposes under the [Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license](https://creativecommons.org/licenses/by-nc/4.0/). The use of the dataset for purposes such as competitions and commercial purposes needs prior written permission. In all documents and papers that use or refer to the dataset or report experimental results based on the Kvasir-VQA, a reference to the related article needs to be added: 
+
+```
+@article{Gautam2024Sep,
+	author = {Gautam, Sushant and Stor{\aa}s, Andrea and Midoglu, Cise and Hicks, Steven A. and Thambawita, Vajira and Halvorsen, P{\aa}l and Riegler, Michael A.},
+	title = {{Kvasir-VQA: A Text-Image Pair GI Tract Dataset}},
+	journal = {arXiv},
+	year = {2024},
+	month = sep,
+	eprint = {2409.01437},
+	doi = {10.48550/arXiv.2409.01437}
+}
+
+@inproceedings{gautam2024kvasirvqa,
+  title={Kvasir-VQA: A Text-Image Pair GI Tract Dataset},
+  author={Gautam, Sushant and Storås, Andrea and Midoglu, Cise and Hicks, Steven A. and Thambawita, Vajira and Halvorsen, Pål and Riegler, Michael A.},
+  booktitle={Proceedings of the First International Workshop on Vision-Language Models for Biomedical Applications (VLM4Bio '24)},
+  year={2024},
+  location={Melbourne, VIC, Australia},
+  pages={10 pages},
+  publisher={ACM},
+  doi={10.1145/3689096.3689458}
+}
+```
+
+## Contact
+Please contact [email protected], [email protected], [email protected] or [email protected] for any questions regarding the dataset.

licenses/SOURCE_TERMS.md

@@ -9,3 +9,8 @@ PolypGen: preserve both conflicting original notices supplied here. The archive
 CVC-ClinicDB: no CVC image or mask bytes are redistributed in this repository. Only the previously audited external-test identity manifest is included. Original rights remain with Hospital Clinic and CVC. Obtain data from the original author channel and comply with applicable access/use terms: https://pages.cvc.uab.es/CVC-Colon/index.php/databases/ and https://pages.cvc.uab.es/ai4polypnet/wp-content/uploads/2024/05/AgreementData_CVC-1.pdf . The agreement was located but its complete current text could not be retrieved during this publication; this is not a finding that all redistribution is prohibited.
 
 Derived weak text labels and curation contributions are supplied for noncommercial research and education under CC BY-NC-SA 4.0, subject to all original image terms. This does not relicense Kvasir or CVC. https://creativecommons.org/licenses/by-nc-sa/4.0/
+
+
+## Kvasir-VQA extension
+
+The separate vqa_sft configuration uses original Kvasir-VQA images and atomic annotations by Gautam et al., source revision 26df9125b98cbad664e5b1801aac1f70a02689e2. CC BY-NC 4.0 applies to these source materials. The source card also requests prior permission for competitions/commercial use. Cite https://doi.org/10.48550/arXiv.2409.01437 and https://datasets.simula.no/kvasir-vqa. Fixed English sentence rendering and question paraphrases are our transformations; no x1 LLM-generated labels are included. This does not relicense the original materials under PolypGen terms. Read Kvasir-VQA_SOURCE_CARD.md.

vqa_sft/annotation_conflicts.json

@@ -0,0 +1 @@
+[]

vqa_sft/audit.json

@@ -0,0 +1,213 @@
+{
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+}

vqa_sft/exclusions.json

@@ -0,0 +1,3327 @@
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vqa_sft/selected_image_ids.json

@@ -0,0 +1,858 @@
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+    "cl8k2u1q51ejb0832cwyf32qe"
+  ]
+}

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vqa_sft/train.parquet

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vqa_sft/val.parquet

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