Patherea: Cell Detection and Classification for the 2020s

• URL: http://arxiv.org/abs/2412.16425v2
• Date: Wed, 16 Jul 2025 11:52:13 GMT
• Title: Patherea: Cell Detection and Classification for the 2020s
• Authors: Dejan Štepec, Maja Jerše, Snežana Đokić, Jera Jeruc, Nina Zidar, Danijel Skočaj,
• Abstract summary: Patherea is a unified framework for point-based cell detection and classification.<n>Our method directly predicts cell locations and classes without relying on intermediate representations.<n>Patherea achieves state-of-the-art performance on public datasets.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Patherea, a unified framework for point-based cell detection and classification that enables the development and fair evaluation of state-of-the-art methods. To support this, we introduce a large-scale dataset that replicates the clinical workflow for Ki-67 proliferation index estimation. Our method directly predicts cell locations and classes without relying on intermediate representations. It incorporates a hybrid Hungarian matching strategy for accurate point assignment and supports flexible backbones and training regimes, including recent pathology foundation models. Patherea achieves state-of-the-art performance on public datasets - Lizard, BRCA-M2C, and BCData - while highlighting performance saturation on these benchmarks. In contrast, our newly proposed Patherea dataset presents a significantly more challenging benchmark. Additionally, we identify and correct common errors in current evaluation protocols and provide an updated benchmarking utility for standardized assessment. The Patherea dataset and code are publicly available to facilitate further research and fair comparisons.

Related papers

• Clustering by Nonparametric Smoothing [6.635604919499181]
  A novel formulation of the clustering problem is introduced in which the task is expressed as an estimation problem. The proposed approach bypasses any explicit modelling assumptions and exploits the flexible estimation potential of nonparametric smoothing. Experiments on a large collection of publicly available data sets are used to document the strong performance of the proposed approach.
  arXiv  Detail & Related papers  (2025-03-12T07:44:11Z)
• Revisiting BPR: A Replicability Study of a Common Recommender System Baseline [78.00363373925758]
  We study the features of the BPR model, indicating their impact on its performance, and investigate open-source BPR implementations. Our analysis reveals inconsistencies between these implementations and the original BPR paper, leading to a significant decrease in performance of up to 50% for specific implementations. We show that the BPR model can achieve performance levels close to state-of-the-art methods on the top-n recommendation tasks and even outperform them on specific datasets.
  arXiv  Detail & Related papers  (2024-09-21T18:39:53Z)
• Rethinking the Effectiveness of Graph Classification Datasets in Benchmarks for Assessing GNNs [7.407592553310068]
  We propose an empirical protocol based on a fair benchmarking framework to investigate the performance discrepancy between simple methods and GNNs. We also propose a novel metric to quantify the dataset effectiveness by considering both dataset complexity and model performance. Our findings shed light on the current understanding of benchmark datasets, and our new platform could fuel the future evolution of graph classification benchmarks.
  arXiv  Detail & Related papers  (2024-07-06T08:33:23Z)
• Proper Dataset Valuation by Pointwise Mutual Information [26.693741797887643]
  We propose an information-theoretic framework for evaluating data curation methods.<n>We define dataset quality in terms of its informativeness about the true model parameters.<n>We show that the Blackwell order can be determined by the Shannon mutual information between the curated data and the test data.
  arXiv  Detail & Related papers  (2024-05-28T15:04:17Z)
• Minimally Supervised Learning using Topological Projections in Self-Organizing Maps [55.31182147885694]
  We introduce a semi-supervised learning approach based on topological projections in self-organizing maps (SOMs) Our proposed method first trains SOMs on unlabeled data and then a minimal number of available labeled data points are assigned to key best matching units (BMU) Our results indicate that the proposed minimally supervised model significantly outperforms traditional regression techniques.
  arXiv  Detail & Related papers  (2024-01-12T22:51:48Z)
• When is Off-Policy Evaluation (Reward Modeling) Useful in Contextual Bandits? A Data-Centric Perspective [64.73162159837956]
  evaluating the value of a hypothetical target policy with only a logged dataset is important but challenging. We propose DataCOPE, a data-centric framework for evaluating a target policy given a dataset. Our empirical analysis of DataCOPE in the logged contextual bandit settings using healthcare datasets confirms its ability to evaluate both machine-learning and human expert policies.
  arXiv  Detail & Related papers  (2023-11-23T17:13:37Z)
• Sample Complexity of Preference-Based Nonparametric Off-Policy Evaluation with Deep Networks [58.469818546042696]
  We study the sample efficiency of OPE with human preference and establish a statistical guarantee for it. By appropriately selecting the size of a ReLU network, we show that one can leverage any low-dimensional manifold structure in the Markov decision process.
  arXiv  Detail & Related papers  (2023-10-16T16:27:06Z)
• Open-Set Domain Adaptation with Visual-Language Foundation Models [51.49854335102149]
  Unsupervised domain adaptation (UDA) has proven to be very effective in transferring knowledge from a source domain to a target domain with unlabeled data. Open-set domain adaptation (ODA) has emerged as a potential solution to identify these classes during the training phase.
  arXiv  Detail & Related papers  (2023-07-30T11:38:46Z)
• Universal Domain Adaptation from Foundation Models: A Baseline Study [58.51162198585434]
  We make empirical studies of state-of-the-art UniDA methods using foundation models. We introduce textitCLIP distillation, a parameter-free method specifically designed to distill target knowledge from CLIP models. Although simple, our method outperforms previous approaches in most benchmark tasks.
  arXiv  Detail & Related papers  (2023-05-18T16:28:29Z)
• Going beyond research datasets: Novel intent discovery in the industry setting [60.90117614762879]
  This paper proposes methods to improve the intent discovery pipeline deployed in a large e-commerce platform. We show the benefit of pre-training language models on in-domain data: both self-supervised and with weak supervision. We also devise the best method to utilize the conversational structure (i.e., question and answer) of real-life datasets during fine-tuning for clustering tasks, which we call Conv.
  arXiv  Detail & Related papers  (2023-05-09T14:21:29Z)
• A Meta-Learning Approach to Predicting Performance and Data Requirements [163.4412093478316]
  We propose an approach to estimate the number of samples required for a model to reach a target performance. We find that the power law, the de facto principle to estimate model performance, leads to large error when using a small dataset. We introduce a novel piecewise power law (PPL) that handles the two data differently.
  arXiv  Detail & Related papers  (2023-03-02T21:48:22Z)
• RGB-D-Based Categorical Object Pose and Shape Estimation: Methods, Datasets, and Evaluation [5.71097144710995]
  This work provides an overview of the field in terms of methods, datasets, and evaluation protocols. We take a critical look at the predominant evaluation protocol, including metrics and datasets. We propose a new set of metrics, contribute new annotations for the Redwood dataset, and evaluate state-of-the-art methods in a fair comparison.
  arXiv  Detail & Related papers  (2023-01-19T15:59:10Z)
• Image Classification with Small Datasets: Overview and Benchmark [0.0]
  We systematically organize and connect past studies to consolidate a community that is currently fragmented and scattered. We propose a common benchmark that allows for an objective comparison of approaches. We use this benchmark to re-evaluate the standard cross-entropy baseline and ten existing methods published between 2017 and 2021 at renowned venues.
  arXiv  Detail & Related papers  (2022-12-23T17:11:16Z)
• Comparison of Model-Free and Model-Based Learning-Informed Planning for PointGoal Navigation [10.797100163772482]
  We compare state-of-the-art Deep Reinforcement Learning based approaches with Partially Observable Markov Decision Process (POMDP) formulation of the point goal navigation problem. We show comparable, though slightly worse performance than the SOTA DD-PPO approach, yet with far fewer data.
  arXiv  Detail & Related papers  (2022-12-17T05:23:54Z)
• Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition [94.56304526014875]
  We propose the first Source-Free Unsupervised Domain Adaptation (SFUDA) method for Facial Expression Recognition (FER) Our method exploits self-supervised pretraining to learn good feature representations from the target data. We validate the effectiveness of our method in four adaptation setups, proving that it consistently outperforms existing SFUDA methods when applied to FER.
  arXiv  Detail & Related papers  (2022-10-11T08:24:50Z)
• A Recommendation Approach based on Similarity-Popularity Models of Complex Networks [1.385805101975528]
  This work proposes a novel recommendation method based on complex networks generated by a similarity-popularity model to predict ones. We first construct a model of a network having users and items as nodes from observed ratings and then use it to predict unseen ratings. The proposed approach is implemented and experimentally compared against baseline and state-of-the-art recommendation methods on 21 datasets from various domains.
  arXiv  Detail & Related papers  (2022-09-29T11:00:06Z)
• A Closer Look at Debiased Temporal Sentence Grounding in Videos: Dataset, Metric, and Approach [53.727460222955266]
  Temporal Sentence Grounding in Videos (TSGV) aims to ground a natural language sentence in an untrimmed video. Recent studies have found that current benchmark datasets may have obvious moment annotation biases. We introduce a new evaluation metric "dR@n,IoU@m" that discounts the basic recall scores to alleviate the inflating evaluation caused by biased datasets.
  arXiv  Detail & Related papers  (2022-03-10T08:58:18Z)
• Multi-view Data Classification with a Label-driven Auto-weighted Strategy [32.581793437017716]
  We propose an auto-weighted strategy to evaluate the importance of views from a label perspective. Based on this strategy, we propose a transductive semi-supervised auto-weighted multi-view classification model. The proposed method achieves optimal or sub-optimal classification accuracy at the lowest computational cost.
  arXiv  Detail & Related papers  (2022-01-03T15:27:54Z)
• Deep Adversarial Domain Adaptation Based on Multi-layer Joint Kernelized Distance [30.452492118887182]
  Domain adaptation refers to the learning scenario that a model learned from the source data is applied on the target data. The distribution discrepancy between source data and target data can substantially affect the adaptation performance. A deep adversarial domain adaptation model based on a multi-layer joint kernelized distance metric is proposed.
  arXiv  Detail & Related papers  (2020-10-09T02:32:48Z)
• BREEDS: Benchmarks for Subpopulation Shift [98.90314444545204]
  We develop a methodology for assessing the robustness of models to subpopulation shift. We leverage the class structure underlying existing datasets to control the data subpopulations that comprise the training and test distributions. Applying this methodology to the ImageNet dataset, we create a suite of subpopulation shift benchmarks of varying granularity.
  arXiv  Detail & Related papers  (2020-08-11T17:04:47Z)

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.