Semi-Supervised Learning (SSL) is important for reducing the annotation cost for medical image segmentation models. State-of-the-art SSL methods such as Mean Teacher, FixMatch and Cross Pseudo Supervision (CPS) are mainly based on consistency regularization or pseudo-label supervision between a reference prediction and a supervised prediction. Despite the effectiveness, they have overlooked the potential noise in the labeled data, and mainly focus on strategies to generate the reference prediction, while ignoring the heterogeneous values of different unlabeled pixels. We argue that effectively mining the rich information contained by the two predictions in the loss function, instead of the specific strategy to obtain a reference prediction, is more essential for SSL, and propose a universal framework MetaSSL based on a spatially heterogeneous loss that assigns different weights to pixels by simultaneously leveraging the uncertainty and consistency information between the reference and supervised predictions. Specifically, we split the predictions on unlabeled data into four regions with decreasing weights in the loss: Unanimous and Confident (UC), Unanimous and Suspicious (US), Discrepant and Confident (DC), and Discrepant and Suspicious (DS), where an adaptive threshold is proposed to distinguish confident predictions from suspicious ones. The heterogeneous loss is also applied to labeled images for robust learning considering the potential annotation noise. Our method is plug-and-play and general to most existing SSL methods. The experimental results showed that it improved the segmentation performance significantly when integrated with existing SSL frameworks on different datasets. Code is available at https://github.com/HiLab-git/MetaSSL.

Classification of pathological images is the basis for automatic cancer diagnosis. Despite that deep learning methods have achieved remarkable performance, they heavily rely on labeled data, demanding extensive human annotation efforts. In this study, we present a novel human annotation-free method by leveraging pre-trained Vision-Language Models (VLMs). Without human annotation, pseudo-labels of the training set are obtained by utilizing the zero-shot inference capabilities of VLM, which may contain a lot of noise due to the domain gap between the pre-training and target datasets. To address this issue, we introduce VLM-CPL, a novel approach that contains two noisy label filtering techniques with a semi-supervised learning strategy. Specifically, we first obtain prompt-based pseudo-labels with uncertainty estimation by zero-shot inference with the VLM using multiple augmented views of an input. Then, by leveraging the feature representation ability of VLM, we obtain feature-based pseudo-labels via sample clustering in the feature space. Prompt-feature consensus is introduced to select reliable samples based on the consensus between the two types of pseudo-labels. We further propose High-confidence Cross Supervision by to learn from samples with reliable pseudo-labels and the remaining unlabeled samples. Additionally, we present an innovative open-set prompting strategy that filters irrelevant patches from whole slides to enhance the quality of selected patches. Experimental results on five public pathological image datasets for patch-level and slide-level classification showed that our method substantially outperformed zero-shot classification by VLMs, and was superior to existing noisy label learning methods. The code is publicly available at https://github.com/HiLab-git/VLM-CPL.

Accurate segmentation of multiple abdominal organs from Computed Tomography (CT) images plays an important role in computer-aided diagnosis, treatment planning and follow-up. Currently, 3D Convolution Neural Networks (CNN) have achieved promising performance for automatic medical image segmentation tasks. However, most existing 3D CNNs have a large set of parameters and huge floating point operations (FLOPs), and 3D CT volumes have a large size, leading to high computational cost, which limits their clinical application. To tackle this issue, we propose a novel framework based on lightweight network and Knowledge Distillation (KD) for delineating multiple organs from 3D CT volumes. We first propose a novel lightweight medical image segmentation network named LCOV-Net for reducing the model size and then introduce two knowledge distillation modules (i.e., Class-Affinity KD and Multi-Scale KD) to effectively distill the knowledge from a heavy-weight teacher model to improve LCOV-Net's segmentation accuracy. Experiments on two public abdominal CT datasets for multiple organ segmentation showed that: 1) Our LCOV-Net outperformed existing lightweight 3D segmentation models in both computational cost and accuracy; 2) The proposed KD strategy effectively improved the performance of the lightweight network, and it outperformed existing KD methods; 3) Combining the proposed LCOV-Net and KD strategy, our framework achieved better performance than the state-of-the-art 3D nnU-Net with only one-fifth parameters. The code is available at https://github.com/HiLab-git/LCOVNet-and-KD.

Semantic segmentation of histopathological images is important for automatic cancer diagnosis, and it is challenged by time-consuming and labor-intensive annotation process that obtains pixel-level labels for training. To reduce annotation costs, Weakly Supervised Semantic Segmentation (WSSS) aims to segment objects by only using image or patch-level classification labels. Current WSSS methods are mostly based on Class Activation Map (CAM) that usually locates the most discriminative object part with limited segmentation accuracy. In this work, we propose a novel two-stage weakly supervised segmentation framework based on High-resolution Activation Maps and Interleaved Learning (HAMIL). First, we propose a simple yet effective Classification Network with High-resolution Activation Maps (HAM-Net) that exploits a lightweight classification head combined with Multiple Layer Fusion (MLF) of activation maps and Monte Carlo Augmentation (MCA) to obtain precise foreground regions. Second, we use dense pseudo labels generated by HAM-Net to train a better segmentation model, where three networks with the same structure are trained with interleaved learning: The agreement between two networks is used to highlight reliable pseudo labels for training the third network, and at the same time, the two networks serve as teachers for guiding the third network via knowledge distillation. Extensive experiments on two public histopathological image datasets of lung cancer demonstrated that our proposed HAMIL outperformed state-of-the-art weakly supervised and noisy label learning methods, respectively. The code is available at https://github.com/HiLab-git/HAMIL.

High frequency oscillations (HFOs) have demonstrated their potency acting as an effective biomarker in epilepsy. However, most of the existing HFOs detectors are based on manual feature extraction and supervised learning, which incur laborious feature selection and time-consuming labeling process. In order to tackle these issues, we propose an automatic unsupervised HFOs detector based on convolutional variational autoencoder (CVAE). First, each selected HFO candidate (via an initial detection method) is converted into a 2-D time-frequency map (TFM) using continuous wavelet transform (CWT). Then, CVAE is trained on the red channel of the TFM (R-TFM) dataset so as to achieve the goal of dimensionality reduction and reconstruction of input feature. The reconstructed R-TFM dataset is later classified by K-means algorithm. Experimental results show that the proposed method outperforms four existing detectors, and achieve 92.85% in accuracy, 93.91% in sensitivity, and 92.14% in specificity.