Datasets:

torchmil
/

PANDA_MIL

Tasks:

Image Classification

Languages:

English

License:

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xet

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The dataset generation failed

Error code:   DatasetGenerationError
Exception:    ArrowInvalid
Message:      Cannot mix NumPy dtypes float64 and float32
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1586, in _prepare_split_single
                  writer.write(example, key)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 553, in write
                  self.write_examples_on_file()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 511, in write_examples_on_file
                  self.write_batch(batch_examples=batch_examples)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 627, in write_batch
                  arrays.append(pa.array(typed_sequence))
                File "pyarrow/array.pxi", line 247, in pyarrow.lib.array
                File "pyarrow/array.pxi", line 112, in pyarrow.lib._handle_arrow_array_protocol
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 244, in __arrow_array__
                  out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))
                File "pyarrow/array.pxi", line 343, in pyarrow.lib.array
                File "pyarrow/array.pxi", line 42, in pyarrow.lib._sequence_to_array
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Cannot mix NumPy dtypes float64 and float32
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1595, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 658, in finalize
                  self.write_examples_on_file()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 511, in write_examples_on_file
                  self.write_batch(batch_examples=batch_examples)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 627, in write_batch
                  arrays.append(pa.array(typed_sequence))
                File "pyarrow/array.pxi", line 247, in pyarrow.lib.array
                File "pyarrow/array.pxi", line 112, in pyarrow.lib._handle_arrow_array_protocol
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 244, in __arrow_array__
                  out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))
                File "pyarrow/array.pxi", line 343, in pyarrow.lib.array
                File "pyarrow/array.pxi", line 42, in pyarrow.lib._sequence_to_array
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Cannot mix NumPy dtypes float64 and float32
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1451, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 994, in stream_convert_to_parquet
                  builder._prepare_split(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1447, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1604, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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PANDA - Multiple Instance Learning (MIL)

Important. This dataset is part of the torchmil library.

This repository provides an adapted version of the Prostate cANcer graDe Assessment (PANDA) dataset tailored for Multiple Instance Learning (MIL). It is designed for use with the PANDAMILDataset class from the torchmil library. PANDA is a widely used benchmark in MIL research, making this adaptation particularly valuable for developing and evaluating MIL models.

About the original PANDA Dataset

The original PANDA dataset contains WSIs of hematoxylin and eosin (H&E) stained prostate biopsy samples. The task is to classify the severity of prostate cancer within each slide, and to localize the cancerous tissue precisely. The dataset includes high-quality pixel-level annotations marking the cancerous tissue.

Dataset Description

We have preprocessed the whole-slide images (WSIs) by extracting relevant patches and computing features for each patch using various feature extractors.

A patch is labeled as positive (patch_label=1) if more than 50% of its pixels are annotated as cancerous.
A WSI is labeled as positive (label=1) if it contains at least one positive patch.

This means a slide is considered positive if there is any evidence of cancerous tissue.

Directory Structure

After extracting the contents of the .tar.gz archives, the following directory structure is expected:

root
├── patches_{patch_size}
│ ├── features
│ │ ├── features_{features_name}
│ │ │ ├── wsi1.npy
│ │ │ ├── wsi2.npy
│ │ │ └── ...
│ ├── labels
│ │ ├── wsi1.npy
│ │ ├── wsi2.npy
│ │ └── ...
│ ├── patch_labels
│ │ ├── wsi1.npy
│ │ ├── wsi2.npy
│ │ └── ...
│ ├── coords
│ │ ├── wsi1.npy
│ │ ├── wsi2.npy
│ │ └── ...
└── splits.csv

Each .npy file corresponds to a single WSI. The splits.csv file defines train/test splits for standardized experimentation.

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