Sk-Unet Model with Fourier Domain for Mitosis Detection

• URL: http://arxiv.org/abs/2109.00957v1
• Date: Wed, 1 Sep 2021 17:10:39 GMT
• Title: Sk-Unet Model with Fourier Domain for Mitosis Detection
• Authors: Sen Yang, Feng Luo, Jun Zhang, Xiyue Wang
• Abstract summary: Mitotic count is the most important morphological feature of breast cancer grading. Many deep learning-based methods have been proposed but suffer from domain shift. In this work, we construct a Fourier-based segmentation model for mitosis detection to address the problem.
• Score: 7.237569333193943
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mitotic count is the most important morphological feature of breast cancer grading. Many deep learning-based methods have been proposed but suffer from domain shift. In this work, we construct a Fourier-based segmentation model for mitosis detection to address the problem. Swapping the low-frequency spectrum of source and target images is shown effective to alleviate the discrepancy between different scanners. Our Fourier-based segmentation method can achieve F1 with 0.7456 on the preliminary test set.

Related papers

• Benchmarking histopathology foundation models in a multi-center dataset for skin cancer subtyping [1.927195358774599]
  Pretraining on large-scale, in-domain datasets grants histopathology foundation models (FM) the ability to learn task-agnostic data representations.<n>In computational pathology, automated whole slide image analysis requires multiple instance learning (MIL) frameworks due to the gigapixel scale of the slides.<n>Our work presents a novel benchmark for evaluating histopathology FMs as patch-level feature extractors within a MIL classification framework.
  arXiv  Detail & Related papers  (2025-06-23T14:12:16Z)
• PathSegDiff: Pathology Segmentation using Diffusion model representations [63.20694440934692]
  We propose PathSegDiff, a novel approach for histopathology image segmentation that leverages Latent Diffusion Models (LDMs) as pre-trained featured extractors. Our method utilizes a pathology-specific LDM, guided by a self-supervised encoder, to extract rich semantic information from H&E stained histopathology images. Our experiments demonstrate significant improvements over traditional methods on the BCSS and GlaS datasets.
  arXiv  Detail & Related papers  (2025-04-09T14:58:21Z)
• Domain Adaptive Synapse Detection with Weak Point Annotations [63.97144211520869]
  We present AdaSyn, a framework for domain adaptive synapse detection with weak point annotations. In the WASPSYN challenge at I SBI 2023, our method ranks the 1st place.
  arXiv  Detail & Related papers  (2023-08-31T05:05:53Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• FFCNet: Fourier Transform-Based Frequency Learning and Complex Convolutional Network for Colon Disease Classification [6.483399675485918]
  We propose a Fourier-based Frequency Complex Network (FFCNet) for colon disease classification. FFCNet is a novel complex network that enables the combination of complex convolutional networks with frequency learning. Our method achieves high performance outperforming previous state-of-the art methods with an accuracy of 86:35% and an accuracy of 4.46% higher than the backbone.
  arXiv  Detail & Related papers  (2022-07-04T09:32:23Z)
• 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)
• Adaptation to CT Reconstruction Kernels by Enforcing Cross-domain Feature Maps Consistency [0.06117371161379209]
  We show a decrease in the COVID-19 segmentation quality of the model trained on the smooth and tested on the sharp reconstruction kernels. We propose the unsupervised adaptation method, called F-Consistency, that outperforms the previous approaches.
  arXiv  Detail & Related papers  (2022-03-28T10:00:03Z)
• Robust Multi-Domain Mitosis Detection [0.0]
  We learn a target representative feature space through unpaired image to image translation (CycleGAN) This work presents a simple yet effective multi-step mitotic figure detection algorithm developed as a baseline for the MIDOG challenge.
  arXiv  Detail & Related papers  (2021-09-13T06:25:15Z)
• A novel deep learning-based method for monochromatic image synthesis from spectral CT using photon-counting detectors [7.190103828139802]
  We propose a novel deep learning-based monochromatic image synthesis method working in sinogram domain. Our method was tested on a cone-beam CT (CBCT) system equipped with a PCD.
  arXiv  Detail & Related papers  (2020-07-20T03:44:57Z)
• FDA: Fourier Domain Adaptation for Semantic Segmentation [82.4963423086097]
  We describe a simple method for unsupervised domain adaptation, whereby the discrepancy between the source and target distributions is reduced by swapping the low-frequency spectrum of one with the other. We illustrate the method in semantic segmentation, where densely annotated images are aplenty in one domain, but difficult to obtain in another. Our results indicate that even simple procedures can discount nuisance variability in the data that more sophisticated methods struggle to learn away.
  arXiv  Detail & Related papers  (2020-04-11T22:20:48Z)
• Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis [64.82680813427054]
  Plant diseases serve as one of main threats to food security and crop production. One popular approach is to transform this problem as a leaf image classification task, which can be addressed by the powerful convolutional neural networks (CNNs) We propose a novel framework that incorporates rectified meta-learning module into common CNN paradigm to train a noise-robust deep network without using extra supervision information.
  arXiv  Detail & Related papers  (2020-03-17T09:51:30Z)
• Data Augmentation for Histopathological Images Based on Gaussian-Laplacian Pyramid Blending [59.91656519028334]
  Data imbalance is a major problem that affects several machine learning (ML) algorithms. In this paper, we propose a novel approach capable of not only augmenting HI dataset but also distributing the inter-patient variability. Experimental results on the BreakHis dataset have shown promising gains vis-a-vis the majority of DA techniques presented in the literature.
  arXiv  Detail & Related papers  (2020-01-31T22:02:57Z)

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.