Blur was naturally analyzed in the frequency domain, by estimating the latent sharp image and the blur kernel given a blurry image. Recent progress on image deblurring always designs end-to-end architectures and aims at learning the difference between blurry and sharp image pairs from pixel-level, which inevitably overlooks the importance of blur kernels. This paper reveals an intriguing phenomenon that simply applying ReLU operation on the frequency domain of a blur image followed by inverse Fourier transform, i.e., frequency selection, provides faithful information about the blur pattern (e.g., the blur direction and blur level, implicitly shows the kernel pattern). Based on this observation, we attempt to leverage kernel-level information for image deblurring networks by inserting Fourier transform, ReLU operation, and inverse Fourier transform to the standard ResBlock. 1 × 1 convolution is further added to let the network modulate flexible thresholds for frequency selection. We term our newly built block as Res FFT-ReLU Block, which takes advantages of both kernel-level and pixel-level features via learning frequency-spatial dual-domain representations. Extensive experiments are conducted to acquire a thorough analysis on the insights of the method. Moreover, after plugging the proposed block into NAFNet, we can achieve 33.85 dB in PSNR on GoPro dataset. Our method noticeably improves backbone architectures without introducing many parameters, while maintaining low computational complexity. Code is available at https://github.com/DeepMed-Lab/DeepRFT-AAAI2023.

Hyperspectral imaging (HSI) unlocks the huge potential to a wide variety of applications relying on high-precision pathology image segmentation, such as computational pathology. It can acquire biochemical properties even invisible to naked eyes from histological specimens. Since 1) spectra contain discriminative and continuous patterns for differentiating tissues/cells, and 2) the discriminability of spectra relies on both fine-grained relations in the high-resolution spectrum and coarse relations in the low-resolution spectrum, the key to achieving high-precision hyperspectral pathology image segmentation is to felicitously model the intra- and inter-scale context especially for spectra. In this paper, we propose a spectral transformer (SpecTr) for hyperspectral pathology image segmentation, which first captures global context for intra-scale spectral features, and subsequently extract coarse and fine-grained discriminative spectral information from inter-scale features, respectively. To learn intra-scale spectral context, we propose a Spectral Attentive Module (SAM). Unlike the existing Transformer model that is designed for modalities such as natural images, our proposed SAM is efficient in capturing sparse and pivotal spectral context while avoiding the heterogeneous underlying distributions and noises of different bands. Besides, to reduce the computational complexity of the HSI segmentation model, we further propose a global-local attention module to effectively learn a condensed spectral feature. Experiments show that HSIs can become a more powerful image modality for understanding microscopic pathology images than RGB images, and the proposed SpecTr outperforms other competing methods for hyperspectral pathology image segmentation, with an improvement of 3% compared with the popular 3D-nnUNet and other transformer-based methods. Our code is available at https://github.com/DeepMed-Lab-ECNU/SpecTr.

Due to the computational complexity of self-attention (SA), prevalent techniques for image deblurring often resort to either adopting localized SA or employing coarse-grained global SA methods, both of which exhibit drawbacks such as compromising global modeling or lacking fine-grained correlation. In order to address this issue by effectively modeling long-range dependencies without sacrificing fine-grained details, we introduce a novel approach termed Local Frequency Transformer (LoFormer). Within each unit of LoFormer, we incorporate a Local Channel-wise SA in the frequency domain (Freq-LC) to simultaneously capture cross-covariance within low- and high-frequency local windows. These operations offer the advantage of (1) ensuring equitable learning opportunities for both coarse-grained structures and fine-grained details, and (2) exploring a broader range of representational properties compared to coarse-grained global SA methods. Additionally, we introduce an MLP Gating mechanism complementary to Freq-LC, which serves to filter out irrelevant features while enhancing global learning capabilities. Our experiments demonstrate that LoFormer significantly improves performance in the image deblurring task, achieving a PSNR of 34.09 dB on the GoPro dataset with 126G FLOPs. https://github.com/DeepMed-Lab-ECNU/Single-Image-Deblur

A multiscale deep learning method for quantitative visualization of traumatic hemoperitoneum showed significantly improved accuracy for prediction of a composite outcome of surgical or angiographic... PurposeTo evaluate the feasibility of a multiscale deep learning algorithm for quantitative visualization and measurement of traumatic hemoperitoneum and to compare diagnostic performance for relev...

Medical imaging is limited by acquisition time and scanning equipment. CT and MR volumes, reconstructed with thicker slices, are anisotropic with high in-plane resolution and low through-plane resolution. We reveal an intriguing phenomenon that due to the mentioned nature of data, performing slice-wise interpolation from the axial view can yield greater benefits than performing super-resolution from other views. Based on this observation, we propose an Inter-Intra-slice Interpolation Network (I ³ Net), which fully explores information from high in-plane resolution and compensates for low through-plane resolution. The through-plane branch supplements the limited information contained in low through-plane resolution from high in-plane resolution and enables continual and diverse feature learning. In-plane branch transforms features to the frequency domain and enforces an equal learning opportunity for all frequency bands in a global context learning paradigm. We further propose a cross-view block to take advantage of the information from all three views online. Extensive experiments on two public datasets demonstrate the effectiveness of I ³ Net, and noticeably outperforms state-of-the-art super-resolution, video frame interpolation and slice interpolation methods by a large margin. We achieve 43.90dB in PSNR, with at least 1.14dB improvement under the upscale factor of ×2 on MSD dataset with faster inference.