Although multi-instance learning (MIL) has succeeded in pathological image classification, it faces the challenge of high inference costs due to processing numerous patches from gigapixel whole slide images (WSIs). To address this, we propose HDMIL, a hierarchical distillation multi-instance learning framework that achieves fast and accurate classification by eliminating irrelevant patches. HD-MIL consists of two key components: the dynamic multi-instance network (DMIN) and the lightweight instance pre-screening network (LIPN). DMIN operates on high-resolution WSIs, while LIPN operates on the corresponding low-resolution counterparts. During training, DMIN are trained for WSI classification while generating attention-score-based masks that indicate irrelevant patches. These masks then guide the training of LIPN to predict the relevance of each low-resolution patch. During testing, LIPN first determines the useful regions within low-resolution WSIs, which indirectly enables us to eliminate irrelevant regions in high-resolution WSIs, thereby reducing inference time without causing performance degradation. In addition, we further design the first Chebyshev-polynomials-based Kolmogorov-Arnold classifier in computational pathology, which enhances the performance of HDMIL through learnable activation layers. Extensive experiments on three public datasets demonstrate that HDMIL outperforms previous state-of-the-art methods, e.g., achieving improvements of 3.13% in AUC while reducing inference time by 28.6% on the Camelyon16 dataset. The project is available at https://github.com/JiuyangDong/HDMIL.

In recent years, virtual immunohistochemical (IHC) staining, which converts hematoxylin and eosin (H&E) images into IHC images, has emerged as a promising technology in digital histopathology. Most existing methods rely on paired H&E and IHC patches extracted from adjacent tissue sections for supervised training. However, tissue misalignment and tissue loss between adjacent sections lead to inconsistent training pairs, limiting the models' ability to produce accurate staining results. To address this issue, we propose ConCLR, a two-stage virtual IHC staining framework based on context-aware contrastive learning, designed to handle inconsistently paired patches. Our method is built on the assumption that for a given mini-patch in the H&E patch, there may exist a corresponding mini-patch in the reference IHC patch exhibiting a similar Pos/Neg pathological pattern. If such a mini-patch exists, it is typically located spatially close to the H&E mini-patch due to the local consistency of tissue structure. In the first stage, we leverage this assumption to design a similarity-guided mini-patch sampling (SGMS) module. For each mini-patch anchor in the staining results, SGMS searches within the real IHC patch to find the most similar mini-patch to serve as the positive sample for contrastive learning, enabling effective supervision despite mild tissue misalignment. In the second stage, we design a context-aware adaptive refinement module, which addresses significant inconsistencies between training pairs caused by potential tissue loss, by expanding the search range of positive samples to include neighboring patches. Extensive experiments on two network backbones across four virtual IHC staining tasks demonstrate the effectiveness of our ConCLR. Evaluations include qualitative and quantitative assessments of staining results, as well as downstream diagnostic performance. In addition to experiments on existing public datasets, we collected a PanCK-NSCLC dataset by acquiring H&E and pan-cytokeratin staining images from the same lung tissue sections via destaining and restaining. This dataset offers significantly improved tissue alignment compared to those derived from adjacent sections, with the aim of facilitating further progress in virtual IHC staining.

As the predominant approach for pathological whole slide image (WSI) classification, multiple instance learning (MIL) methods struggle with limited labeled WSIs. Although MIL has achieved notable progress with pseudo-bag-oriented augmentation methods, their effectiveness is often constrained by noisy pseudo-labels and low-quality pseudo-bags. To overcome these problems, we revisit the use of pseudo-bags for WSI data augmentation and propose a new pseudo-bag generation paradigm, dubbed DPBAug. Its distinctive features can be summarized as: i) We develop an intra-slide pseudo-bag generation module, which separates the heterogeneous instances within each slide through phenotype partitioning. Moreover, to ensure accurate label inheritance when generating pseudo-bags, we propose an instance sampling algorithm with replacement. ii) An inter-slide pseudo-bag fusion module is designed to integrate heterogeneous information across multiple WSIs, producing high-quality training samples that better leverage the potential of neural networks. iii) A pseudo-bag memory update module prioritizes valuable synthetic pseudo-bags. This further enhances the network's classification performance. Extensive experiments demonstrate that DPBAug surpasses existing augmentation methods, enhancing the classification performance and reliability of multiple MIL baselines across various public datasets. DPBAug also improves the generalization and data efficiency of existing MIL methods, facilitating their adoption in clinical practice and rare cancer research The project is available at: https://github.com/JiuyangDong/DPBAug.

Recently, virtual staining technology has greatly promoted the advancement of histopathology. Despite the practical successes achieved, the outstanding performance of most virtual staining methods relies on hard-to-obtain paired images in training. In this paper, we propose a method for virtual immunohistochemistry (IHC) staining, named confusion-GAN, which does not require paired images and can achieve comparable performance to supervised algorithms. Specifically, we propose a multi-branch discriminator, which judges if the features of generated images can be embedded into the feature pool of target domain images, to improve the visual quality of generated images. Meanwhile, we also propose a novel patch-level pathology information extractor, which is assisted by multiple instance learning, to ensure pathological consistency during virtual staining. Extensive experiments were conducted on three types of IHC images, including a high-resolution hepatocel-lular carcinoma immunohistochemical dataset proposed by us. The results demonstrated that our proposed confusion-GAN can generate highly realistic images that are capable of deceiving even experienced pathologists. Furthermore, compared to using H&E images directly, the downstream diagnosis achieved higher accuracy when using images generated by confusion-GAN. Our dataset and codes will be available at https://github.com/jiahanli2022/confusion-GAN.

Confocal microscopy is the de-facto standard technique in bio-imaging for acquiring 3D images in the presence of tissue scattering. However, the point-scanning mechanism inherent in confocal microscopy implies that the capture speed is much too slow for imaging dynamic objects at sufficient spatial resolution and signal to noise ratio(SNR). In this paper, we propose an algorithm for super-resolution confocal microscopy that allows us to capture high-resolution, high SNR confocal images at an order of magnitude faster acquisition speed. The proposed Back-Projection Generative Adversarial Network (BPGAN) consists of a feature extraction step followed by a back-projection feedback module (BPFM) and an associated reconstruction network, these together allow for super-resolution of low-resolution confocal scans. We validate our method using real confocal captures of multiple biological specimens and the results demonstrate that our proposed BPGAN is able to achieve similar quality to high-resolution confocal scans while the imaging speed can be up to 64 times faster.