Add pages for volume v254

Gemfile

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+source "https://rubygems.org"
+
+git_source(:github) {|repo_name| "https://github.com/#{repo_name}" }
+
+gem 'jekyll'
+
+group :jekyll_plugins do
+  gem 'github-pages'
+  gem 'jekyll-remote-theme'
+  gem 'jekyll-include-cache'
+  gem 'webrick'
+end
+
+# gem "rails"
+

README.md

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+# PMLR 254
+
+To suggest fixes to this volume please make a pull request containing the changes requested and a justification for the changes.
+
+To edit the details of this conference work edit the [_config.yml](./_config.yml) file and submit a pull request.
+
+To make changes to the individual paper details, edit the associated paper file in the [./_posts](./_posts) subdirectory.
+
+For details of how to publish in PMLR please check https://proceedings.mlr.press/faq.html
+
+For details of what is required to submit a proceedings please check https://proceedings.mlr.press/spec.html
+
+
+
+Published as Volume 254 by the Proceedings of Machine Learning Research on 17 November 2024.
+
+Volume Edited by:
+  * Francesco Ciompi
+  * Nadieh Khalili
+  * Linda Studer
+  * Milda Poceviciute
+  * Amjad Khan
+  * Mitko Veta
+  * Yiping Jiao
+  * Neda Haj-Hosseini
+  * Hao Chen
+  * Shan Raza
+  * Fayyaz Minhas
+  * Inti Zlobec
+  * Nikolay Burlutskiy
+  * Veronica Vilaplana
+  * Biagio Brattoli
+  * Henning Muller
+  * Manfredo Atzori
+  * Shan Raza
+  * Fayyaz Minhas
+
+Series Editors:
+  * Neil D. Lawrence

_config.yml

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+---
+booktitle: Proceedings of the MICCAI Workshop on Computational Pathology
+shortname: MICCAI COMPAYL 2024
+year: '2024'
+start: &1 2024-10-06
+end: 2024-10-06
+published: 2024-11-17
+publisher: PMLR
+series: Proceedings of Machine Learning Research
+volume: '254'
+layout: proceedings
+issn: 2640-3498
+id: COMPAY2024
+month: 0
+cycles: false
+bibtex_editor: Ciompi, Francesco and Khalili, Nadieh and Studer, Linda and Poceviciute,
+  Milda and Khan, Amjad and Veta, Mitko and Jiao, Yiping and Haj-Hosseini, Neda and
+  Chen, Hao and Raza, Shan and Minhas, Fayyaz and Zlobec, Inti and Burlutskiy, Nikolay
+  and Vilaplana, Veronica and Brattoli, Biagio and Muller, Henning and Atzori, Manfredo
+  and Raza, Shan and Minhas, Fayyaz
+editor:
+- given: Francesco
+  family: Ciompi
+- given: Nadieh
+  family: Khalili
+- given: Linda
+  family: Studer
+- given: Milda
+  family: Poceviciute
+- given: Amjad
+  family: Khan
+- given: Mitko
+  family: Veta
+- given: Yiping
+  family: Jiao
+- given: Neda
+  family: Haj-Hosseini
+- given: Hao
+  family: Chen
+- given: Shan
+  family: Raza
+- given: Fayyaz
+  family: Minhas
+- given: Inti
+  family: Zlobec
+- given: Nikolay
+  family: Burlutskiy
+- given: Veronica
+  family: Vilaplana
+- given: Biagio
+  family: Brattoli
+- given: Henning
+  family: Muller
+- given: Manfredo
+  family: Atzori
+- given: Shan
+  family: Raza
+- given: Fayyaz
+  family: Minhas
+title: Proceedings of Machine Learning Research
+description: |
+  Proceedings of the MICCAI Workshop on Computational Pathology
+    Held in Marrakesh, Morocco on 06 October 2024
+
+  Published as Volume 254 by the Proceedings of Machine Learning Research on 17 November 2024.
+
+  Volume Edited by:
+    Francesco Ciompi
+    Nadieh Khalili
+    Linda Studer
+    Milda Poceviciute
+    Amjad Khan
+    Mitko Veta
+    Yiping Jiao
+    Neda Haj-Hosseini
+    Hao Chen
+    Shan Raza
+    Fayyaz Minhas
+    Inti Zlobec
+    Nikolay Burlutskiy
+    Veronica Vilaplana
+    Biagio Brattoli
+    Henning Muller
+    Manfredo Atzori
+    Shan Raza
+    Fayyaz Minhas
+
+  Series Editors:
+    Neil D. Lawrence
+date_str: 06 Oct
+url: https://proceedings.mlr.press
+author:
+  name: PMLR
+baseurl: "/v254"
+twitter_username: MLResearchPress
+github_username: mlresearch
+markdown: kramdown
+exclude:
+- README.md
+- Gemfile
+- ".gitignore"
+plugins:
+- jekyll-feed
+- jekyll-seo-tag
+- jekyll-remote-theme
+remote_theme: mlresearch/jekyll-theme
+style: pmlr
+permalink: "/:title.html"
+ghub:
+  edit: true
+  repository: v254
+display:
+  copy_button:
+    bibtex: true
+    endnote: true
+    apa: true
+  comments: false
+volume_type: Volume
+volume_dir: v254
+email: ''
+conference:
+  name: Proceedings of the MICCAI Workshop on Computational Pathology
+  url:
+  location: Marrakesh, Morocco
+  dates:
+  - *1
+analytics:
+  google:
+    tracking_id: UA-92432422-1
+orig_bibfile: "/Users/neil/mlresearch/v254/bibliography.bib"
+# Site settings
+# Original source:  /Users/neil/mlresearch/v254/bibliography.bib

_posts/2024-11-17-abdo24a.md

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+---
+title: 'StairwayToStain: A Gradual Stain Translation Approach for Glomeruli Segmentation'
+booktitle: Proceedings of the MICCAI Workshop on Computational Pathology
+abstract: 'Image-to-image translation (I2I) has advanced digital pathology by enabling
+  knowledge transfer across clinical contexts through unsupervised domain adaptation
+  (UDA). Although promising, most I2I frameworks transfer source-labeled data to target
+  unlabeled data directly in a one-off way. However, translating stains from information-poor
+  domains to information-rich ones can lead to a domain shift problem due to the large
+  discrepancy between domains. To address this issue, we propose StairwayToStain (STS),
+  an unsupervised gradual stain translation framework that uses intermediate stains
+  to bridge the gap between the source and target stain. Our method is grounded in
+  three main phases: (i) measuring the domain shift between different stains, (ii)
+  defining a translation path, and (iii) performing the gradual stain translation.
+  Our method demonstrates its efficacy in improving glomeruli segmentation when translating
+  from immunohistochemical (IHC) to histochemical stains, as well as between different
+  IHC stains. Comprehensive experiments on stain translation demonstrate STS’s competitive
+  results compared to its variants and state-of-the-art direct I2I methods in achieving
+  UDA. Moreover, we are able to generate additional stains during the translation
+  process. Our method presents the first framework for gradual domain adaptation in
+  stain translation.'
+layout: inproceedings
+series: Proceedings of Machine Learning Research
+publisher: PMLR
+issn: 2640-3498
+id: abdo24a
+month: 0
+tex_title: 'StairwayToStain: A Gradual Stain Translation Approach for Glomeruli Segmentation'
+firstpage: 180
+lastpage: 191
+page: 180-191
+order: 180
+cycles: false
+bibtex_author: Abdo, Ali Alhaj and Mhiri, Islem and Nisar, Zeeshan and Seeliger, Barbara
+  and Lampert, Thomas
+author:
+- given: Ali Alhaj
+  family: Abdo
+- given: Islem
+  family: Mhiri
+- given: Zeeshan
+  family: Nisar
+- given: Barbara
+  family: Seeliger
+- given: Thomas
+  family: Lampert
+date: 2024-11-17
+address:
+container-title: Proceedings of the MICCAI Workshop on Computational Pathology
+volume: '254'
+genre: inproceedings
+issued:
+  date-parts:
+  - 2024
+  - 11
+  - 17
+pdf: https://raw.githubusercontent.com/mlresearch/v254/main/assets/abdo24a/abdo24a.pdf
+extras: []
+# Format based on Martin Fenner's citeproc: https://blog.front-matter.io/posts/citeproc-yaml-for-bibliographies/
+---

_posts/2024-11-17-ahmed24a.md

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+---
+title: 'PathAlign: A vision–language model for whole slide images in histopathology'
+booktitle: Proceedings of the MICCAI Workshop on Computational Pathology
+abstract: Microscopic interpretation of histopathology images underlies many important
+  diagnostic and treatment decisions. While advances in vision–language modeling raise
+  new oppor- tunities for analysis of such images, the gigapixel-scale size of whole
+  slide images (WSIs) introduces unique challenges. Additionally, pathology reports
+  simultaneously highlight key findings from small regions while also aggregating
+  interpretation across multiple slides, often making it difficult to create robust
+  image–text pairs. As such, pathology reports remain a largely untapped source of
+  supervision in computational pathology, with most efforts relying on region-of-interest
+  annotations or self-supervision at the patch-level. In this work, we develop a vision–language
+  model based on the BLIP-2 framework using WSIs paired with curated text from pathology
+  reports. This enables applications utilizing a shared image–text embedding space,
+  such as text or image retrieval for finding cases of interest, as well as integration
+  of the WSI encoder with a frozen large language model (LLM) for WSI-based generative
+  text capabilities such as report generation or AI-in-the-loop interactions. We utilize
+  a de-identified dataset of over 350,000 WSIs and diagnostic text pairs, spanning
+  a wide range of diagnoses, procedure types, and tissue types. We present pathologist
+  evaluation of text generation and text retrieval using WSI embeddings, as well as
+  results for WSI classification and workflow prioritization (slide-level triaging).
+  Model-generated text for WSIs was rated by pathologists as accurate, without clinically
+  significant error or omission, for 78% of WSIs on average. This work demonstrates
+  exciting potential capabilities for language-aligned WSI embeddings.
+layout: inproceedings
+series: Proceedings of Machine Learning Research
+publisher: PMLR
+issn: 2640-3498
+id: ahmed24a
+month: 0
+tex_title: 'PathAlign: A vision–language model for whole slide images in histopathology'
+firstpage: 72
+lastpage: 108
+page: 72-108
+order: 72
+cycles: false
+bibtex_author: Ahmed, Faruk and Sellergen, Andrew and Yang, Lin and Xu, Shawn and
+  Babenko, Boris and Ward, Abbi and Olson, Niels and Mohtashamian, Arash and Matias,
+  Yossi and Corrado, Greg S. and Duong, Quang and Webster, Dale R. and Shetty, Shravya
+  and Golden, Daniel and Liu, Yun and Steiner, David F. and Wulczyn, Ellery
+author:
+- given: Faruk
+  family: Ahmed
+- given: Andrew
+  family: Sellergen
+- given: Lin
+  family: Yang
+- given: Shawn
+  family: Xu
+- given: Boris
+  family: Babenko
+- given: Abbi
+  family: Ward
+- given: Niels
+  family: Olson
+- given: Arash
+  family: Mohtashamian
+- given: Yossi
+  family: Matias
+- given: Greg S.
+  family: Corrado
+- given: Quang
+  family: Duong
+- given: Dale R.
+  family: Webster
+- given: Shravya
+  family: Shetty
+- given: Daniel
+  family: Golden
+- given: Yun
+  family: Liu
+- given: David F.
+  family: Steiner
+- given: Ellery
+  family: Wulczyn
+date: 2024-11-17
+address:
+container-title: Proceedings of the MICCAI Workshop on Computational Pathology
+volume: '254'
+genre: inproceedings
+issued:
+  date-parts:
+  - 2024
+  - 11
+  - 17
+pdf: https://raw.githubusercontent.com/mlresearch/v254/main/assets/ahmed24a/ahmed24a.pdf
+extras: []
+# Format based on Martin Fenner's citeproc: https://blog.front-matter.io/posts/citeproc-yaml-for-bibliographies/
+---