Update README.md

README.md

@@ -73,24 +73,49 @@ configs:
 ## Abstract
 Clinical diagnosis of the eye is performed over multifarious data modalities including scalar clinical labels, vectorized biomarkers, two-dimensional fundus images, and three-dimensional Optical Coherence Tomography (OCT) scans. While the clinical labels, fundus images and OCT scans are instrumental measurements, the vectorized biomarkers are interpreted attributes from the other measurements. Clinical practitioners use all these data modalities for diagnosing and treating eye diseases like Diabetic Retinopathy (DR) or Diabetic Macular Edema (DME). Enabling usage of machine learning algorithms within the ophthalmic medical domain requires research into the relationships and interactions between these relevant data modalities. Existing datasets are limited in that: ($i$) they view the problem as disease prediction without assessing biomarkers, and ($ii$) they do not consider the explicit relationship among all four data modalities over the treatment period. In this paper, we introduce the Ophthalmic Labels for Investigating Visual Eye Semantics (OLIVES) dataset that addresses the above limitations. This is the first OCT and fundus dataset that includes clinical labels, biomarker labels, and time-series patient treatment information from associated clinical trials. The dataset consists of $1268$ fundus eye images each with $49$ OCT scans, and $16$ biomarkers, along with $3$ clinical labels and a disease diagnosis of DR or DME. In total, there are 96 eyes' data averaged over a period of at least two years with each eye treated for an average of 66 weeks and 7 injections. OLIVES dataset has advantages in other fields of machine learning research including self-supervised learning as it provides alternate augmentation schemes that are medically grounded.
 
-**Labels**:
-There are two directories for the labels: full_labels and ml_centric labels. 
+## Subsets
+There are 3 subsets included in this dataset:
+- Disease Classification (`disease_classification`)
+
+### Disease Classification
+This subset contains information regarding 78,000+ OCT scans obtained from a series of visits patients performed. In terms of labels, there are:
+
+- `Image`: An image of the OCT scan
+- `BCVA`: Best Central Visual Acuity
+- `CST`: Central Subfield Thickness
+- `Patient ID`: A value to help distinguish different patients
+- `Disease Label`: A value of `0` for DR (Diabetic Retinopathy) and `1` for DME (Diabetic Macular Edema)
+
+This information can be used to classify the disease. In addition, the first and last visit of a patient included extra biomarker information. This can be summarized into these 16 mostly-boolean labels:
+
+- `Scan (n/49)`: The scan number out of the 49 scans taken for each patient
+- `Atrophy / thinning of retinal layer`
+- `Disruption of EZ`: Disruption of Ellipsoid Zone
+- `DRIL`: Disruption of Retinal Inner Layers
+- `IR hemorrhages`: Intraretinal hemorrhages
+- `IR HRF`: Intraretinal Hyperreflective Foci
+- `Partially attached vitreous face`
+- `Fully attached vitreous face`
+- `Preretinal tissue/hemorrhage`
+- `Vitreous debris`
+- `VMT`: Vitreomacular Traction
+- `DRT/ME`: Diffuse Retinal Thickening or Macular Edema
+- `Fluid (IRF)`: Intraretinal Fluid
+- `Fluid (SRF)`: Subretinal Fluid
+- `Disruption of RPE`: Disruption of Retinal Pigment Epithelium
+- `PED (serous)`: Pigment Epithelial Detachment
+- `SHRM`: Subretinal Hyperreflective Material
+- `Eye_ID`: A value to help distinguish different eye scans (should be the same as `Patient ID`)
 
-**Full labels** contain all the clinical inforamtion used in these studies for the associated studies of interest.
-
-**ML Centric labels** are divided into two files: Biomarker_Clinical_Data_Images.csv and Clinical_Data_Images.xlsx. 
-
-Biomarker_Clinical_Data_Images.csv contains full biomarker and clinical labels for the 9408 images that have this labeled biomarker information.
-
-Clinical_Data_Images.xlsx has the BCVA, CST, Eye ID, and Patient ID for the 78000+ images that have clinical data. 
 
 ## Data Download
+Sample code to download the disease classification dataset:
 
 ```python
 from datasets import load_dataset
 from torch.utils.data import DataLoader
 
-olives = load_dataset('gOLIVES/OLIVES_dataset',split='train')
+olives = load_dataset('gOLIVES/OLIVES_dataset', 'disease_classification', split = 'train')
 
 # Covert into a Format Usable by Pytorch
 olives = olives.with_format("torch")
@@ -104,6 +129,11 @@ print(olives[0]['VMT'])
 
 ```
 
+## Known Issues
+- Patient ID #79 has missing `BCVA` and `CST` for most visits except the first and last visit as the biomarker information is present
+- Certain visits for patients seem to have the exact same scans as a previous visit. For instance Patient ID #61 has identical images in W8 and their next visit in W12.
+
+
 ## Links
 
 **Associated Website**: https://ghassanalregib.info/
@@ -112,10 +142,9 @@ print(olives[0]['VMT'])
 
 If you find the work useful, please include the following citation in your work:
 
->@inproceedings{prabhushankarolives2022,\
-  title={OLIVES Dataset: Ophthalmic Labels for Investigating
-Visual Eye Semantics},\
-  author={Prabhushankar, Mohit and Kokilepersaud, Kiran and Logan, Yash-yee and Trejo Corona, Stephanie and AlRegib, Ghassan and Wykoff, Charles},\
-  booktitle={Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 2 (NeurIPS Datasets and Benchmarks 2022) },\
-  year={2022}\
-}
+> @inproceedings{prabhushankarolives2022,\
+>     title={OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics},\
+>     author={Prabhushankar, Mohit and Kokilepersaud, Kiran and Logan, Yash-yee and Trejo Corona, Stephanie and AlRegib, Ghassan and Wykoff, Charles},\
+>     booktitle={Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 2 (NeurIPS Datasets and Benchmarks 2022)},\
+>     year={2022}\
+> }