C3R: Channel Conditioned Cell Representations for unified evaluation in microscopy imaging

• URL: http://arxiv.org/abs/2505.18745v1
• Date: Sat, 24 May 2025 15:24:05 GMT
• Title: C3R: Channel Conditioned Cell Representations for unified evaluation in microscopy imaging
• Abstract summary: We introduce a structured view of cellular image channels by grouping them into either context or concept.<n>We develop Channel Conditioned Cell Representations (C3R), a framework designed for unified evaluation on IHC datasets.<n>C3R is a two-fold framework comprising a channel-adaptive encoder architecture and a masked knowledge distillation training strategy.
• Score: 4.157406649742161
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Immunohistochemical (IHC) images reveal detailed information about structures and functions at the subcellular level. However, unlike natural images, IHC datasets pose challenges for deep learning models due to their inconsistencies in channel count and configuration, stemming from varying staining protocols across laboratories and studies. Existing approaches build channel-adaptive models, which unfortunately fail to support out-of-distribution (OOD) evaluation across IHC datasets and cannot be applied in a true zero-shot setting with mismatched channel counts. To address this, we introduce a structured view of cellular image channels by grouping them into either context or concept, where we treat the context channels as a reference to the concept channels in the image. We leverage this context-concept principle to develop Channel Conditioned Cell Representations (C3R), a framework designed for unified evaluation on in-distribution (ID) and OOD datasets. C3R is a two-fold framework comprising a channel-adaptive encoder architecture and a masked knowledge distillation training strategy, both built around the context-concept principle. We find that C3R outperforms existing benchmarks on both ID and OOD tasks, while a trivial implementation of our core idea also outperforms the channel-adaptive methods reported on the CHAMMI benchmark. Our method opens a new pathway for cross-dataset generalization between IHC datasets, without requiring dataset-specific adaptation or retraining.

Related papers

• ChorusTIC: Training-Free Multivariate Time Series Classification via Chorus In-Context Learning [52.72143039958434]
  We introduce ChorusTIC, a classification-native foundation model for in-context classification across heterogeneous channel configurations.<n>ChorusTIC maps variable channel configurations into a fixed-width representation independent of the original channel count.<n>It calibrates feature axes using context-derived distributions and predicts query labels through leakage-protected in-context learning.
  arXiv  Detail & Related papers  (2026-08-25T03:45:24Z)
• SegDem: Segmentation helps Demosaicing [52.515779223891705]
  Image demosaicing reconstructs a full-color image from incomplete color measurements produced by a sensor covered with a color filter (CFA)<n>Most existing methods formulate demosaicing as pixel-level reconstruction.<n>SegDem is a cross-task decoder representation transfer framework for demosaicing.
  arXiv  Detail & Related papers  (2026-08-08T04:49:20Z)
• DiCE-CIR: Direct Composition Learning for Efficient Zero-Shot Composed Image Retrieval [52.15696141238419]
  Zero-shot composed image retrieval (ZS-CIR) aims to retrieve a target image from a multimodal query consisting of a reference image and an edit text.<n>We propose DiCE-CIR, a direct composition learning method that predicts composed query representations by directly composing a reference image and an edit text.
  arXiv  Detail & Related papers  (2026-07-06T04:37:30Z)
• Channel-Free Human Activity Recognition via Inductive-Bias-Aware Fusion Design for Heterogeneous IoT Sensor Environments [0.0]
  Human activity recognition (HAR) in Internet of Things (IoT) environments must cope with heterogeneous sensor settings.<n>We investigate strict channel-free HAR, in which a single shared model performs inference without assuming a fixed number, order, or semantic arrangement of input channels.<n>We propose a channel-free HAR framework that combines channel-wise encoding with a shared encoder, metadata-conditioned late fusion, and joint optimization of channel-level and fused predictions.
  arXiv  Detail & Related papers  (2026-04-23T07:32:36Z)
• CAMEL-CLIP: Channel-aware Multimodal Electroencephalography-text Alignment for Generalizable Brain Foundation Models [5.527305245660595]
  We propose a multimodal EEG-text alignment contrastive language-image pretraining model (CAMEL-CLIP)<n>We show that CAMEL-CLIP achieves state-of-the-art performance under linear-probing and outperforms existing foundation models that rely on full-finetuning.
  arXiv  Detail & Related papers  (2026-02-27T05:05:08Z)
• CSMCIR: CoT-Enhanced Symmetric Alignment with Memory Bank for Composed Image Retrieval [54.15776146365823]
  Composed Image Retrieval (CIR) enables users to search for target images using both a reference image and manipulation text.<n>We propose CSMCIR, a unified representation framework that achieves efficient query-target alignment through three synergistic components.
  arXiv  Detail & Related papers  (2026-01-07T09:21:38Z)
• ISCS: Parameter-Guided Channel Ordering and Grouping for Learned Image Compression [11.645256028223272]
  We propose a dataset-agnostic method to identify and organize important channels in pretrained LIC models.<n>Our method effectively reduces computation while maintaining reconstruction quality, providing a practical and modular enhancement to existing learned compression frameworks.
  arXiv  Detail & Related papers  (2025-09-21T00:44:15Z)
• Leveraging CORAL-Correlation Consistency Network for Semi-Supervised Left Atrium MRI Segmentation [14.296441810235223]
  Semi-supervised learning (SSL) has been widely used to learn from both a few labeled images and many unlabeled images. Most current SSL-based segmentation methods use pixel values directly to identify similar features in labeled and unlabeled data. We introduce CORAL(Correlation-Aligned)-Correlation Consistency Network (CORN) to capture the global structure shape and local details of Left Atrium.
  arXiv  Detail & Related papers  (2024-10-21T11:46:28Z)
• An I2I Inpainting Approach for Efficient Channel Knowledge Map Construction [33.648491554358]
  We propose a Laplacian pyramid (LP)-based CKM construction scheme to predict the channel knowledge at arbitrary locations in a targeted area. The proposed approach has a strong generalization ability and can be implemented in different wireless communication scenarios.
  arXiv  Detail & Related papers  (2024-06-14T08:24:52Z)
• Cross-SRN: Structure-Preserving Super-Resolution Network with Cross Convolution [64.76159006851151]
  It is challenging to restore low-resolution (LR) images to super-resolution (SR) images with correct and clear details. Existing deep learning works almost neglect the inherent structural information of images. We design a hierarchical feature exploitation network to probe and preserve structural information.
  arXiv  Detail & Related papers  (2022-01-05T05:15:01Z)
• Semi-Supervised Learning for Channel Charting-Aided IoT Localization in Millimeter Wave Networks [97.66522637417636]
  A novel framework is proposed for channel charting (CC)-aided localization in millimeter wave networks. In particular, a convolutional autoencoder model is proposed to estimate the three-dimensional location of wireless user equipment. The framework is extended to a semi-supervised framework, where the autoencoder is divided into two components.
  arXiv  Detail & Related papers  (2021-08-03T14:41:38Z)
• Channel-Wise Early Stopping without a Validation Set via NNK Polytope Interpolation [36.479195100553085]
  Convolutional neural networks (ConvNets) comprise high-dimensional feature spaces formed by the aggregation of multiple channels. We present channel-wise DeepNNK, a novel generalization estimate based on non-dimensional kernel regression (NNK) graphs.
  arXiv  Detail & Related papers  (2021-07-27T17:33:30Z)
• 3D Human Action Representation Learning via Cross-View Consistency Pursuit [52.19199260960558]
  We propose a Cross-view Contrastive Learning framework for unsupervised 3D skeleton-based action Representation (CrosSCLR) CrosSCLR consists of both single-view contrastive learning (SkeletonCLR) and cross-view consistent knowledge mining (CVC-KM) modules, integrated in a collaborative learning manner.
  arXiv  Detail & Related papers  (2021-04-29T16:29:41Z)
• Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation using Structurally Regularized Deep Clustering [119.88565565454378]
  Unsupervised domain adaptation (UDA) is to learn classification models that make predictions for unlabeled data on a target domain. We propose a hybrid model of Structurally Regularized Deep Clustering, which integrates the regularized discriminative clustering of target data with a generative one. Our proposed H-SRDC outperforms all the existing methods under both the inductive and transductive settings.
  arXiv  Detail & Related papers  (2020-12-08T08:52:00Z)
• Channel Attention Residual U-Net for Retinal Vessel Segmentation [8.109768170171357]
  We propose a new deep learning model, namely Channel Attention Residual U-Net (CAR-UNet) to accurately segment retinal vascular and non-vascular pixels. The results show that our proposed CAR-UNet has reached the state-of-the-art performance on three publicly available retinal vessel datasets.
  arXiv  Detail & Related papers  (2020-04-07T20:47:40Z)
• Channel Interaction Networks for Fine-Grained Image Categorization [61.095320862647476]
  Fine-grained image categorization is challenging due to the subtle inter-class differences. We propose a channel interaction network (CIN), which models the channel-wise interplay both within an image and across images. Our model can be trained efficiently in an end-to-end fashion without the need of multi-stage training and testing.
  arXiv  Detail & Related papers  (2020-03-11T11:51:51Z)
• Channel Equilibrium Networks for Learning Deep Representation [63.76618960820138]
  This work shows that the combination of normalization and rectified linear function leads to inhibited channels. Unlike prior arts that simply removed the inhibited channels, we propose to "wake them up" during training by designing a novel neural building block. Channel Equilibrium (CE) block enables channels at the same layer to contribute equally to the learned representation.
  arXiv  Detail & Related papers  (2020-02-29T09:02:31Z)
• UR2KiD: Unifying Retrieval, Keypoint Detection, and Keypoint Description without Local Correspondence Supervision [16.68130648568593]
  Three related tasks, namely keypoint detection, description, and image retrieval can be jointly tackled using a single unified framework. By leveraging diverse information from sequential layers of a standard ResNet-based architecture, we are able to extract keypoints and descriptors that encode local information. Global information for image retrieval is encoded in an end-to-end pipeline, based on pooling of the aforementioned local responses.
  arXiv  Detail & Related papers  (2020-01-20T21:01:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.