Chao Fan

Chao Fan, Hongyuan Yu, Yan Huang et al.

Despite the progress made in Mamba-based medical image segmentation models, existing methods utilizing unidirectional or multi-directional feature scanning mechanisms struggle to effectively capture dependencies between neighboring positions, limiting the discriminant representation learning of local features. These local features are crucial for medical image segmentation as they provide critical structural information about lesions and organs. To address this limitation, we propose SliceMamba, a simple and effective locally sensitive Mamba-based medical image segmentation model. SliceMamba includes an efficient Bidirectional Slice Scan module (BSS), which performs bidirectional feature slicing and employs varied scanning mechanisms for sliced features with distinct shapes. This design ensures that spatially adjacent features remain close in the scanning sequence, thereby improving segmentation performance. Additionally, to fit the varying sizes and shapes of lesions and organs, we further introduce an Adaptive Slice Search method to automatically determine the optimal feature slice method based on the characteristics of the target data. Extensive experiments on two skin lesion datasets (ISIC2017 and ISIC2018), two polyp segmentation (Kvasir and ClinicDB) datasets, and one multi-organ segmentation dataset (Synapse) validate the effectiveness of our method.

Chao Fan, Zhenyu Yin, Yue Li et al.

Monocular depth estimation using Convolutional Neural Networks (CNNs) has shown impressive performance in outdoor driving scenes. However, self-supervised learning of indoor depth from monocular sequences is quite challenging for researchers because of the following two main reasons. One is the large areas of low-texture regions and the other is the complex ego-motion on indoor training datasets. In this work, our proposed method, named IndoorDepth, consists of two innovations. In particular, we first propose a novel photometric loss with improved structural similarity (SSIM) function to tackle the challenge from low-texture regions. Moreover, in order to further mitigate the issue of inaccurate ego-motion prediction, multiple photometric losses at different stages are used to train a deeper pose network with two residual pose blocks. Subsequent ablation study can validate the effectiveness of each new idea. Experiments on the NYUv2 benchmark demonstrate that our IndoorDepth outperforms the previous state-of-the-art methods by a large margin. In addition, we also validate the generalization ability of our method on ScanNet dataset. Code is availabe at https://github.com/fcntes/IndoorDepth.

Chao Fan, Xibin Jia, Anqi Xiao et al.

Few-Shot Medical Image Segmentation (FSMIS) aims to segment novel classes of medical objects using only a few labeled images. Prototype-based methods have made significant progress in addressing FSMIS. However, they typically generate a single global prototype for the support image to match with the query image, overlooking intra-class variations. To address this issue, we propose a Self-guided Prototype Enhancement Network (SPENet). Specifically, we introduce a Multi-level Prototype Generation (MPG) module, which enables multi-granularity measurement between the support and query images by simultaneously generating a global prototype and an adaptive number of local prototypes. Additionally, we observe that not all local prototypes in the support image are beneficial for matching, especially when there are substantial discrepancies between the support and query images. To alleviate this issue, we propose a Query-guided Local Prototype Enhancement (QLPE) module, which adaptively refines support prototypes by incorporating guidance from the query image, thus mitigating the negative effects of such discrepancies. Extensive experiments on three public medical datasets demonstrate that SPENet outperforms existing state-of-the-art methods, achieving superior performance.