Faithful, Interpretable Chest X-ray Diagnosis with Anti-Aliased B-cos Networks

• URL: http://arxiv.org/abs/2507.16761v2
• Date: Thu, 24 Jul 2025 14:58:44 GMT
• Title: Faithful, Interpretable Chest X-ray Diagnosis with Anti-Aliased B-cos Networks
• Abstract summary: B-cos networks offer a promising solution by replacing standard linear layers with a weight-input alignment mechanism.<n>Standard B-cos models suffer from severe aliasing artifacts in their explanation maps, making them unsuitable for clinical use.<n>We introduce anti-aliasing strategies using FLC and BlurPool to significantly improve explanation quality.
• Score: 13.303234049048426
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Faithfulness and interpretability are essential for deploying deep neural networks (DNNs) in safety-critical domains such as medical imaging. B-cos networks offer a promising solution by replacing standard linear layers with a weight-input alignment mechanism, producing inherently interpretable, class-specific explanations without post-hoc methods. While maintaining diagnostic performance competitive with state-of-the-art DNNs, standard B-cos models suffer from severe aliasing artifacts in their explanation maps, making them unsuitable for clinical use where clarity is essential. In this work, we address these limitations by introducing anti-aliasing strategies using FLCPooling (FLC) and BlurPool (BP) to significantly improve explanation quality. Our experiments on chest X-ray datasets demonstrate that the modified $\text{B-cos}_\text{FLC}$ and $\text{B-cos}_\text{BP}$ preserve strong predictive performance while providing faithful and artifact-free explanations suitable for clinical application in multi-class and multi-label settings. Code available at: GitHub repository (url: https://github.com/mkleinma/B-cos-medical-paper).

Related papers

• Look What the Probes Dragged In! Real-World Chest X-ray Shortcuts in MedCLIP [1.4336070582473752]
  We investigate how real-world shortcuts manifest across different layers of the medical CLIP-based model, MedCLIP.<n>We find that the final linear probes achieve a high AUROC but poor calibration in the models.<n>Patterns consistent with localised shortcuts, such as drains, appear at later layers, while patterns consistent with diffuse shortcuts, such as scanner-specific noise patterns, emerge earlier.
  arXiv  Detail & Related papers  (2026-08-12T14:08:52Z)
• Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals [83.0127582612634]
  Near-boundary Rescue (NSR) is a minimal, plug-and-play modification that retains out-of-bound tokens to recover lost signals.<n>NSR substantially improves training stability and delivers consistent gains over strong baselines such as DAPO and GSPO.
  arXiv  Detail & Related papers  (2026-05-21T16:45:31Z)
• Improving Imbalanced Multi-Label Chest X-Ray Diagnosis via CBAM-Enhanced CNN Backbones [0.0]
  We propose a strategy to integrate Convolutional Block Attention Module (CBAM) into traditional CNN blocks to enhance performance in multi-label classification tasks.<n>Our method achieves a mean AUC of 0.8695 on ChestXray14 dataset, outperforming several state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-05-04T08:28:03Z)
• UHR-Net: An Uncertainty-Aware Hypergraph Refinement Network for Medical Image Segmentation [8.273126013629057]
  We propose an Uncertainty-Aware Hypergraph Refinement Network (UHR-Net)<n>We introduce an Uncertainty-Oriented Instance Contrastive (UO-IC) pretraining strategy that couples geometry-aware copy-paste augmentation with hard-negative mining of lesion-like background regions.<n>Second, we design an Uncertainty-Guided Hypergraph Refinement (UGHR) block, which derives an entropy-based uncertainty map from a coarse probability map to guide hypergraph refinement.
  arXiv  Detail & Related papers  (2026-04-30T16:38:51Z)
• SPEGC: Continual Test-Time Adaptation via Semantic-Prompt-Enhanced Graph Clustering for Medical Image Segmentation [12.0982298854338]
  Continual Test-Time Adaptation (CTTA) aims to enable pre-trained models to adapt to continuously changing unlabeled domains.<n>We propose a CTTA via Semantic-Prompt-Enhanced Graph Clustering (SPEGC) for medical image segmentation.
  arXiv  Detail & Related papers  (2026-03-12T03:22:43Z)
• Gated Differential Linear Attention: A Linear-Time Decoder for High-Fidelity Medical Segmentation [15.30336007288786]
  PVT-GDLA is a decoder-centric Transformer that restores sharp, long-range dependencies at linear time.<n>It achieves state-of-the-art accuracy across CT, MRI, ultrasound, and dermoscopy benchmarks under equal training budgets.
  arXiv  Detail & Related papers  (2026-03-03T08:26:08Z)
• CLFSeg: A Fuzzy-Logic based Solution for Boundary Clarity and Uncertainty Reduction in Medical Image Segmentation [2.28126966226433]
  This paper introduces CLFSeg, an encoder-decoder based framework that aggregates the Fuzzy-Convolutional (FC) module.<n>This module enhances the segmentation performance by identifying local and global features while minimizing the uncertainty, noise, and ambiguity in boundary regions.<n>Our proposed model exhibits exceptional performance on four publicly available datasets, including CVC-ColonDB, CVC-ClinicDB, EtisLaribPolypDB, and ACDC.
  arXiv  Detail & Related papers  (2025-10-28T09:06:27Z)
• Diffusion-based Counterfactual Augmentation: Towards Robust and Interpretable Knee Osteoarthritis Grading [7.748673766536236]
  This paper proposes a novel framework, Diffusion-based Counterfactual Augmentation (DCA), which enhances model robustness and interpretability.<n>The framework effectively converts model uncertainty into a robust training signal, offering a promising pathway to developing more accurate and trustworthy automated diagnostic systems.
  arXiv  Detail & Related papers  (2025-06-18T04:16:28Z)
• Holistic White-light Polyp Classification via Alignment-free Dense Distillation of Auxiliary Optical Chromoendoscopy [14.917217801444794]
  This paper proposes a novel holistic classification framework that leverages full-image diagnosis without requiring polyp localization.<n>The key innovation lies in the Alignment-free Dense Distillation (ADD) module, which enables fine-grained cross-domain knowledge distillation.<n>Our method achieves state-of-the-art performance, relatively outperforming the other approaches by at least 2.5% and 16.2% in AUC.
  arXiv  Detail & Related papers  (2025-05-25T21:09:58Z)
• Clinically-Inspired Hierarchical Multi-Label Classification of Chest X-rays with a Penalty-Based Loss Function [1.264536505250038]
  We present a novel approach to multi-label chest X-ray (CXR) image classification that enhances clinical interpretability.<n>We incorporate hierarchical label groupings to capture clinically meaningful relationships between diagnoses.<n>Our model achieved a mean area under the receiver operating characteristic curve (AUROC) of 0.903 on the test set.
  arXiv  Detail & Related papers  (2025-02-05T20:15:06Z)
• Robust-by-Design Classification via Unitary-Gradient Neural Networks [66.17379946402859]
  The use of neural networks in safety-critical systems requires safe and robust models, due to the existence of adversarial attacks. Knowing the minimal adversarial perturbation of any input x, or, equivalently, the distance of x from the classification boundary, allows evaluating the classification robustness, providing certifiable predictions. A novel network architecture named Unitary-Gradient Neural Network is presented. Experimental results show that the proposed architecture approximates a signed distance, hence allowing an online certifiable classification of x at the cost of a single inference.
  arXiv  Detail & Related papers  (2022-09-09T13:34:51Z)
• A Benchmark for Weakly Semi-Supervised Abnormality Localization in Chest X-Rays [42.1336336144291]
  We propose to train the CXR abnormality localization framework via a weakly semi-supervised strategy, termed Point Beyond Class. The core idea behind our PBC is to learn a robust and accurate mapping from the point annotations to the bounding boxes. Experimental results on RSNA and VinDr-CXR datasets justify the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-09-05T14:36:07Z)
• Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification [74.84221280249876]
  An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
  arXiv  Detail & Related papers  (2022-05-08T15:29:54Z)
• Black-Box Diagnosis and Calibration on GAN Intra-Mode Collapse: A Pilot Study [116.05514467222544]
  Generative adversarial networks (GANs) nowadays are capable of producing images of incredible realism. One concern raised is whether the state-of-the-art GAN's learned distribution still suffers from mode collapse. This paper explores to diagnose GAN intra-mode collapse and calibrate that, in a novel black-box setting.
  arXiv  Detail & Related papers  (2021-07-23T06:03:55Z)
• Discriminator-Free Generative Adversarial Attack [87.71852388383242]
  Agenerative-based adversarial attacks can get rid of this limitation. ASymmetric Saliency-based Auto-Encoder (SSAE) generates the perturbations. The adversarial examples generated by SSAE not only make thewidely-used models collapse, but also achieves good visual quality.
  arXiv  Detail & Related papers  (2021-07-20T01:55:21Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Interpretable COVID-19 Chest X-Ray Classification via Orthogonality Constraint [10.190872613479632]
  We investigate the benefit of using Orthogonal Spheres (OS) constraint for classification of COVID-19 cases from chest X-ray images. Previous studies have demonstrated significant benefits in applying such constraints to deep learning models. Our approach achieves an improvement in accuracy of 1.6% and 4.8% for two- and three-class classification.
  arXiv  Detail & Related papers  (2021-02-02T11:35:28Z)
• Weakly-Supervised Cross-Domain Adaptation for Endoscopic Lesions Segmentation [79.58311369297635]
  We propose a new weakly-supervised lesions transfer framework, which can explore transferable domain-invariant knowledge across different datasets. A Wasserstein quantified transferability framework is developed to highlight widerange transferable contextual dependencies. A novel self-supervised pseudo label generator is designed to equally provide confident pseudo pixel labels for both hard-to-transfer and easy-to-transfer target samples.
  arXiv  Detail & Related papers  (2020-12-08T02:26:03Z)
• Dense Label Encoding for Boundary Discontinuity Free Rotation Detection [69.75559390700887]
  This paper explores a relatively less-studied methodology based on classification. We propose new techniques to push its frontier in two aspects. Experiments and visual analysis on large-scale public datasets for aerial images show the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-11-19T05:42:02Z)

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.