v0.1.26

0.1.26 — 2025-05-07

Removed

• Removed datamodules module
• Removed datasets module

Refactor

• Refactored the whole model interface to be more user-friendly.

Features

• Added a new wsi module, including:

  • A SlideReader class to read patches from a WSI slide.
    • Backends: Openslide, CUCIM
    • Adapted the reader class from HistoPrep library. Props to Jopo666
  • get_sub_grids function to get subgrids from a WSI slide. Can be used to filter the patches. Based on connected components.

• Added a new torch_datasets module, including:

  • WSIDatasetInfer class to run inference directly from WSIs.
    • Adapted the class from HistoPrep library. Props to Jopo666
  • TrainDatasetH5 class to handle training data for the models from a h5 file.
  • TrainDatasetFolder class to handle training data for the models from img and label folders.

• Added a new inference.WsiSegmenter-class to handle the segmentation of WSIs.

• Added a new wsi.inst_merger.InstMerger-class to handle the merging of instance masks at image boundaries.

• Added inst2gdf and sem2gdf functions to utils.vectorize module. These functions convert efficiently instance and semantic masks to GeoDataFrame objects.

• Added FileHandler.to_mat and FileHandler.to_gson save functions that take in a dictionary of model output masks (output from the Inferer-classes) and save it to a .mat or '.feather', '.geojson', '.parquet' files.

Added Dependencies

• Added libpysal dependency
• Added networkx dependency

Removed Dependencies

• Removed lightning dependency
• Removed albumentations dependency

Chore

• Move FolderDatasetInferto torch_datasets module

v0.1.25

0.1.25 — 2024-07-05

Features

• Image encoders are imported now only from timm models.
• Add enc_out_indices to model classes, to enable selecting which layers to use as the encoder outputs.

Removed

• Removed SAM and DINOv2 original implementation image-encoders from this repo. These can be found from timm models these days.
• Removed cellseg_models_pytorch.training module which was left unused after example notebooks were updated.

Examples

• Updated example notebooks.
• Added new example notebooks utilizing UNI foundation model from the MahmoodLab.
• Added new example notebooks utilizing the Prov-GigaPath foundation model from the Microsoft Research.
• NOTE: These examples use the huggingface model hub to load the weights. Permission to use the model weights is required to run these examples.

Chore

• Update timm version to above 1.0.0.

Breaking changes

• Lose support for python 3.9
• The self.encoder in each model is new, thus, models with trained weights from previous versions of the package will not work with this version.

v0.1.24

0.1.24 — 2023-10-13

Style

• Update the Ìnferer.infer() -method api to accept arguments related to saving the model outputs.

Features

• Add CPP-Net. https://arxiv.org/abs/2102.06867

• Add option for mixed precision inference

• Add option to interpolate model outputs to a given size to all of the segmentation models.

• Add DINOv2 Backbone

• Add support for .geojson, .feather, .parquet file formats when running inference.

Docs

• Add CPP-Net example trainng with Pannuke dataset.

Fixes

• Fix resize transformation bug.

v0.1.23

0.1.23 — 2023-08-28

Features

• add a stem-skip module. (Long skip for the input image resolution feature map)

• add UnetTR transformer encoder wrapper class

• add a new Encoder wrapper for timm and unetTR based encoders

• Add stem skip support and upsampling block options to all current model architectures

• Add masking option to all the criterions

• Add MAELoss

• Add BCELoss

• Add base class for transformer based backbones

• Add SAM-VitDet image encoder with support to load pre-trained SAM weights

• Add CellVIT-SAM model.

Docs

• Add notebook example on training Hover-Net with lightning from scratch.

• Add notebook example on training StarDist with lightning from scratch.

• Add notebook example on training CellPose with accelerate from scratch.

• Add notebook example on training OmniPose with accelerate from scratch.

• Add notebook example on finetuning CellVIT-SAM with accelerate.

Fixes

• Fix current TimmEncoder to store feature info

• Fix Up block to support transconv and bilinear upsampling and fix data flow issues.

• Fix StardistUnet class to output all the decoder features.

• Fix Decoder, DecoderStage and long-skip modules to work with up scale factors instead of output dimensions.

v0.1.22

0.1.22 — 2023-07-10

Features

• Add mps (Mac) support for inference
• Add cell class probabilities to saved geojson files

v0.1.21

0.1.21 — 2023-06-12

Features

• Add StrongAugment data augmentation policy to data-loading pipeline: https://arxiv.org/abs/2206.15274

v0.1.20

0.1.20 — 2023-01-13

Fixes

• Enable only writing folder&hdf5 datasets with only images

• Enable writing datasets without patching.

• Add long missing h5 reading utility function to FileHandler

Features

• Add hdf5 input file reading to Inferer classes.

• Add option to write pannuke dataset to h5 db in PannukeDataModule and LizardDataModule.

• Add a generic model builder function get_model to models.__init__.py

• Rewrite segmentation benchmarker. Now it can take in hdf5 datasets.

v0.1.19

0.1.19 — 2023-01-04

Features

• Add pytorch lightning in-built auto_lr_finder option to SegmentationExperiment

v0.1.18

0.1.18 — 2023-01-03

Features

• Add Multi-scale-convolutional-attention (MSCA) module (SegNexT).
• Add TokenMixer & MetaFormer modules.

v0.1.17

0.1.17 — 2022-12-29

Features

• Add transformer modules
• Add exact, slice, and memory efficient (xformers) self attention computations
• Add transformers modules to Decoder modules
• Add common transformer mlp activation functions: star-relu, geglu, approximate-gelu.
• Add Linformer self-attention mechanism.
• Add support for model intialization from yaml-file in MultiTaskUnet.
• Add a new cross-attention long-skip module. Works with long_skip='cross-attn'

Refactor

• Added more verbose error messages for the abstract wrapper-modules in modules.base_modules
• Added more verbose error catching for xformers.ops.memory_efficient_attention.