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Commit c15ea640 authored by André Anjos's avatar André Anjos :speech_balloon:
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[readme,doc,pyproject,scripts] Remove traces of "tuberculosis" exclusivity

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1 merge request!6Making use of LightningDataModule and simplification of data loading
...@@ -8,9 +8,10 @@ SPDX-License-Identifier: GPL-3.0-or-later ...@@ -8,9 +8,10 @@ SPDX-License-Identifier: GPL-3.0-or-later
[![coverage](https://gitlab.idiap.ch/biosignal/software/ptbench/badges/main/coverage.svg)](https://www.idiap.ch/software/biosignal/docs/biosignal/software/ptbench/main/coverage/index.html) [![coverage](https://gitlab.idiap.ch/biosignal/software/ptbench/badges/main/coverage.svg)](https://www.idiap.ch/software/biosignal/docs/biosignal/software/ptbench/main/coverage/index.html)
[![repository](https://img.shields.io/badge/gitlab-project-0000c0.svg)](https://gitlab.idiap.ch/biosignal/software/ptbench) [![repository](https://img.shields.io/badge/gitlab-project-0000c0.svg)](https://gitlab.idiap.ch/biosignal/software/ptbench)
# Active Pulmonary Tuberculosis Detection On Chest X-Rays # Computer-Aided Disease Detection from Medical Data
Benchmarks for training and evaluating deep models for the detection of active Benchmarks of convolutional neural network (CNN) architectures applied to
Pulmonary Tuberculosis from Chest X-Ray imaging. disease detection, including Pulmonary Tuberculosis (TB) detection on chest
X-rays (CXR).
For installation and usage instructions, check-out our documentation. For installation and usage instructions, check-out our documentation.
...@@ -4,14 +4,15 @@ ...@@ -4,14 +4,15 @@
.. _ptbench: .. _ptbench:
========================================================= ====================================================
Active Pulmonary Tuberculosis Detection On Chest X-Rays Computer-Aided Disease Detection from Medical Data
========================================================= ====================================================
.. todolist:: .. todolist::
Benchmarks of convolutional neural network (CNN) architectures applied to Benchmarks of convolutional neural network (CNN) architectures applied to
Pulmonary Tuberculosis (TB) detection on chest X-rays (CXR). disease detection, including Pulmonary Tuberculosis (TB) detection on chest
X-rays (CXR).
Please use the BibTeX reference below to cite this work: Please use the BibTeX reference below to cite this work:
......
...@@ -10,7 +10,7 @@ build-backend = "setuptools.build_meta" ...@@ -10,7 +10,7 @@ build-backend = "setuptools.build_meta"
name = "ptbench" name = "ptbench"
version = "1.0.0b0" version = "1.0.0b0"
requires-python = ">=3.10" requires-python = ">=3.10"
description = "Benchmarks for training and evaluating deep models for the detection of active Pulmonary Tuberculosis from Chest X-Ray imaging." description = "Benchmarks for Computer-Aided Disease Detection from Medical Data."
dynamic = ["readme"] dynamic = ["readme"]
license = { text = "GNU General Public License v3 (GPLv3)" } license = { text = "GNU General Public License v3 (GPLv3)" }
authors = [{ name = "Geoffrey Raposo", email = "geoffrey@raposo.ch" }] authors = [{ name = "Geoffrey Raposo", email = "geoffrey@raposo.ch" }]
......
...@@ -22,7 +22,7 @@ from . import ( ...@@ -22,7 +22,7 @@ from . import (
context_settings=dict(help_option_names=["-?", "-h", "--help"]), context_settings=dict(help_option_names=["-?", "-h", "--help"]),
) )
def cli(): def cli():
"""Active Tuberculosis Detection On Chest X-Ray Images.""" """Image classification benchmark."""
pass pass
......
...@@ -11,6 +11,9 @@ from clapper.logging import setup ...@@ -11,6 +11,9 @@ from clapper.logging import setup
from .click import ConfigCommand from .click import ConfigCommand
# avoids X11/graphical desktop requirement when creating plots
__import__("matplotlib").use("agg")
logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s") logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
...@@ -76,7 +79,7 @@ def evaluate( ...@@ -76,7 +79,7 @@ def evaluate(
threshold: str | float, threshold: str | float,
**_, # ignored **_, # ignored
) -> None: ) -> None:
"""Evaluates predictions (from a model) on a binary classification task.""" """Evaluates predictions (from a model) on a classification task."""
import json import json
import typing import typing
......
...@@ -9,6 +9,9 @@ from clapper.logging import setup ...@@ -9,6 +9,9 @@ from clapper.logging import setup
from .train import reusable_options as training_options from .train import reusable_options as training_options
# avoids X11/graphical desktop requirement when creating plots
__import__("matplotlib").use("agg")
logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s") logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
......
...@@ -117,7 +117,8 @@ def predict( ...@@ -117,7 +117,8 @@ def predict(
parallel, parallel,
**_, **_,
) -> None: ) -> None:
"""Predicts Tuberculosis presence (probabilities) on input images.""" """Runs inference (generates scores) on all input images, using a pre-
trained model."""
import json import json
import shutil import shutil
......
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