# Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histology arXiv: https://arxiv.org/abs/2404.19113 ยท HTML: https://mlsanity.com/paper/2404.19113/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 54 of its 70 references that are in the map. - First posted on arXiv 29 Apr 2024. ## Builds on (most cited first, 30 of 54) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016) - [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019) - [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015) - [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018) - [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017) - [Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2002.08546/) (2020) - [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018) - [Contrastive Adaptation Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1901.00976/) (2019) - [A Brief Review of Domain Adaptation](https://mlsanity.com/paper/2010.03978/) (2020) - [Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization](https://mlsanity.com/paper/1704.04232/) (2017) - [Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach](https://mlsanity.com/paper/1703.08448/) (2017) - [Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation](https://mlsanity.com/paper/2110.04202/) (2021) - [Generalized Source-free Domain Adaptation](https://mlsanity.com/paper/2108.01614/) (2021) - [Adversarial Complementary Learning for Weakly Supervised Object Localization](https://mlsanity.com/paper/1804.06962/) (2018) - [A review of domain adaptation without target labels](https://mlsanity.com/paper/1901.05335/) (2019) - [Attention-based Dropout Layer for Weakly Supervised Object Localization](https://mlsanity.com/paper/1908.10028/) (2019) - [TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization](https://mlsanity.com/paper/2103.14862/) (2021) - [Source-free Domain Adaptation via Avatar Prototype Generation and Adaptation](https://mlsanity.com/paper/2106.15326/) (2021) - [Evaluating Weakly Supervised Object Localization Methods Right](https://mlsanity.com/paper/2001.07437/) (2020) - [A Comprehensive Survey on Source-free Domain Adaptation](https://mlsanity.com/paper/2302.11803/) (2023) - [Source-Free Domain Adaptation via Distribution Estimation](https://mlsanity.com/paper/2204.11257/) (2022) - [Domain Impression: A Source Data Free Domain Adaptation Method](https://mlsanity.com/paper/2102.09003/) (2021) - [Source-Free Unsupervised Domain Adaptation: A Survey](https://mlsanity.com/paper/2301.00265/) (2022) - [Machine learning methods for histopathological image analysis](https://mlsanity.com/paper/1709.00786/) (2017) - [Cross-domain Contrastive Learning for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2106.05528/) (2021) - [VDM-DA: Virtual Domain Modeling for Source Data-free Domain Adaptation](https://mlsanity.com/paper/2103.14357/) (2021) - [Inter-Image Communication for Weakly Supervised Localization](https://mlsanity.com/paper/2008.05096/) (2020) - [Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation](https://mlsanity.com/paper/2102.11614/) (2021) - [Rethinking the Route Towards Weakly Supervised Object Localization](https://mlsanity.com/paper/2002.11359/) (2020) ## Built on by (most cited first, 9 of 9) - [Disentangled Source-Free Personalization for Facial Expression Recognition with Neutral Target Data](https://mlsanity.com/paper/2503.20771/) (2025) - [Personalized Feature Translation for Expression Recognition: An Efficient Source-Free Domain Adaptation Method](https://mlsanity.com/paper/2508.09202/) (2025) - [MuSACo: Multimodal Subject-Specific Selection and Adaptation for Expression Recognition with Co-Training](https://mlsanity.com/paper/2508.12522/) (2025) - [TeD-Loc: Text Distillation for Weakly Supervised Object Localization](https://mlsanity.com/paper/2501.12632/) (2025) - [PixelCAM: Pixel Class Activation Mapping for Histology Image Classification and ROI Localization](https://mlsanity.com/paper/2503.24135/) (2025) - [Low-Rank Expert Merging for Multi-Source Domain Adaptation in Person Re-Identification](https://mlsanity.com/paper/2508.06831/) (2025) - [CLIP-IT: CLIP-based Pairing for Histology Images Classification](https://mlsanity.com/paper/2504.16181/) (2025) - [Selective Prediction Forgetting for Adaptation of Weakly Supervised Localization of Histology Images](https://mlsanity.com/paper/2603.12468/) (2026) - [Test Time Adaptation Methods for Point Cloud Registration in Laparoscopic Surgery](https://mlsanity.com/paper/2608.02883/) (2026) ## Abstract (arXiv) Given the emergence of deep learning, digital pathology has gained popularity for cancer diagnosis based on histology images. Deep weakly supervised object localization (WSOL) models can be trained to classify histology images according to cancer grade and identify regions of interest (ROIs) for interpretation, using inexpensive global image-class annotations. A WSOL model initially trained on some labeled source image data can be adapted using unlabeled target data in cases of significant domain shifts caused by variations in staining, scanners, and cancer type. In this paper, we focus on source-free (unsupervised) domain adaptation (SFDA), a challenging problem where a pre-trained source model is adapted to a new target domain without using any source domain data for privacy and efficiency reasons. SFDA of WSOL models raises several challenges in histology, most notably because they are not intended to adapt for both classification and localization tasks. In this paper, 4 state-of-the-art SFDA methods, each one representative of a main SFDA family, are compared for WSOL in terms of classification and localization accuracy. They are the SFDA-Distribution Estimation, Source HypOthesis Transfer, Cross-Domain Contrastive Learning, and Adaptively Domain Statistics Alignment. Experimental results on the challenging Glas (smaller, breast cancer) and Camelyon16 (larger, colon cancer) histology datasets indicate that these SFDA methods typically perform poorly for localization after adaptation when optimized for classification.