Error code:   ConfigNamesError
Exception:    BadZipFile
Message:      zipfiles that span multiple disks are not supported
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1031, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 996, in dataset_module_factory
                  return HubDatasetModuleFactory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 638, in get_module
                  module_name, default_builder_kwargs = infer_module_for_data_files(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 292, in infer_module_for_data_files
                  split_modules = {
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 293, in <dictcomp>
                  split: infer_module_for_data_files_list(data_files_list, download_config=download_config)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 234, in infer_module_for_data_files_list
                  return infer_module_for_data_files_list_in_archives(data_files_list, download_config=download_config)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 262, in infer_module_for_data_files_list_in_archives
                  for f in xglob(extracted, recursive=True, download_config=download_config)[
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 999, in xglob
                  fs, *_ = url_to_fs(urlpath, **storage_options)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 395, in url_to_fs
                  fs = filesystem(protocol, **inkwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/registry.py", line 293, in filesystem
                  return cls(**storage_options)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 80, in __call__
                  obj = super().__call__(*args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/zip.py", line 62, in __init__
                  self.zip = zipfile.ZipFile(
                File "/usr/local/lib/python3.9/zipfile.py", line 1266, in __init__
                  self._RealGetContents()
                File "/usr/local/lib/python3.9/zipfile.py", line 1329, in _RealGetContents
                  endrec = _EndRecData(fp)
                File "/usr/local/lib/python3.9/zipfile.py", line 286, in _EndRecData
                  return _EndRecData64(fpin, -sizeEndCentDir, endrec)
                File "/usr/local/lib/python3.9/zipfile.py", line 232, in _EndRecData64
                  raise BadZipFile("zipfiles that span multiple disks are not supported")
              zipfile.BadZipFile: zipfiles that span multiple disks are not supported

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Dataset Name

MMF (Multicenter Microscopic Findings)

Dataset Description

We collect a multicenter microscopic findings(MMF) dataset from pathology reports of lung adenocarcinoma, consisting of a total of 827 WSI-report pairs from three different medical centers. Unlike existing WSI report generation datasets, our MMF offers the following advantages: (1) It contains only content that can be observed in the given WSI, eliminating irrelevant information. (2) It is the first Chinese pathology report dataset that addresses the language limitations of existing medical MLLMs and enhances diversity in this research field. (3) It includes multiple medical centers and highlights multicenter discrepancies in pathology report generation.

Code Release

The model is released at https://huggingface.co/yili7eli/WSI-MLLM. The full code is coming in this repo (https://github.com/xmed-lab/WSI-MLLM).

Dataset Structure

MMF ├── GDPH

│ ├── caption.txt

│ ├── caption_en.txt

│ ├── 07cbc764db8b4a42bbc3af68a4a7f5c5.dzi

│ ├── 07cbc764db8b4a42bbc3af68a4a7f5c5_files

│ ├── xxx.dzi

│ └── xxx_files

├── DHSMU

│ ├── caption.txt

│ ├── caption_en.txt

│ ├── xxx.dzi

│ └── xxx_files

└── ZJHSMU

├── caption.txt

├── caption_en.txt

├── xxx.dzi

└── xxx_files

caption.txt is the raw Chinese caption.

caption_en.txt is the translated English caption.

The WSI format is .dzi convert from .svs from libvips, containing a xxx.dzi file and tiles in xxx_files.

Larger number of sub-folder in xxx_files indicates larger magnifications (the largest is 40x, the second largest is 20x, and so on).

The dzi file can be processed directly by the MLLM, while the raw svs file is too large to load.

Supported Tasks and Leaderboards

Report Generation.

In-context learning for WSI.

WSI caption.

Pathological pretraining.

Dataset Details

The MMF consists of three sub-datasets, divided by the name of medical centers, namely GDPH (GuangDong Provincial people’s Hospital), DHSMU (Dongguan Hospital affiliated to Southern Medical University) and ZJHSMU (ZhuJiang Hospital of Southern Medical University). Specifically, GDPH and ZJHSMU are scanned by Aperio-SS12181, where the MPP (microns per pixel) is 0.264168 at the max magnification of 40x. DHSMU is scanned by Aperio-KFBIO at 0.482769 MPP (max 20x magnification). 642 out of 827 WSIs are sourced from GDPH, which is used as the training set, validation set, and internal test set. While DHSMU (92 cases) and ZJHSMU (93 cases) are only used as external test sets to evaluate the report generation performance across different report types. All the collected cases belong to lung adenocarcinoma, where the reports for the same disease show large differences in the content and style, showing the difficulty of this task. Our dataset. These cases range from Jan. 2009 to Sep. 2022, and all cases come from different patients to avoid data leaks. The age of patients ranges from 26 to 85 years, with a mean age of around 59 years. Besides, female cases take the major proportion for GDPH and DHSMU. In addition to the original Chinese report, we also provide an English version via the Google translation to benefit a broader international research community. Only de-identified microscopic findings and WSI were used in this dataset for research purposes.

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