Medical vision-language alignment through cross-modal contrastive learning shows promising performance in image-text matching tasks, such as retrieval and zero-shot classification. However, conventional cross-modal contrastive learning (CLIP-based) methods suffer from suboptimal visual representation capabilities, which also limits their effectiveness in vision-language alignment. In contrast, although the models pretrained via multimodal masked modeling struggle with direct cross-modal matching, they excel in visual representation. To address this contradiction, we propose ALTA (ALign Through Adapting), an efficient medical vision-language alignment method that utilizes only about 8% of the trainable parameters and less than 1/5 of the computational consumption required for masked record modeling. ALTA achieves superior performance in vision-language matching tasks like retrieval and zero-shot classification by adapting the pretrained vision model from masked record modeling. Additionally, we integrate temporal-multiview radiograph inputs to enhance the information consistency between radiographs and their corresponding descriptions in reports, further improving the vision-language alignment. Experimental evaluations show that ALTA outperforms the best-performing counterpart by over 4% absolute points in text-to-image accuracy and approximately 6% absolute points in image-to-text retrieval accuracy. The adaptation of vision-language models during efficient alignment also promotes better vision and language understanding. Code is publicly available at https://github.com/DopamineLcy/ALTA.

Supervised Pix2Pix and unsupervised Cycle-consistency are two modes that dominate the field of medical image-to-image translation. However, neither modes are ideal. The Pix2Pix mode has excellent performance. But it requires paired and well pixel-wise aligned images, which may not always be achievable due to respiratory motion or anatomy change between times that paired images are acquired. The Cycle-consistency mode is less stringent with training data and works well on unpaired or misaligned images. But its performance may not be optimal. In order to break the dilemma of the existing modes, we propose a new unsupervised mode called RegGAN for medical image-to-image translation. It is based on the theory of "loss-correction". In RegGAN, the misaligned target images are considered as noisy labels and the generator is trained with an additional registration network to fit the misaligned noise distribution adaptively. The goal is to search for the common optimal solution to both image-to-image translation and registration tasks. We incorporated RegGAN into a few state-of-the-art image-to-image translation methods and demonstrated that RegGAN could be easily combined with these methods to improve their performances. Such as a simple CycleGAN in our mode surpasses latest NICEGAN even though using less network parameters. Based on our results, RegGAN outperformed both Pix2Pix on aligned data and Cycle-consistency on misaligned or unpaired data. RegGAN is insensitive to noises which makes it a better choice for a wide range of scenarios, especially for medical image-to-image translation tasks in which well pixel-wise aligned data are not available. Code and dataset are available at https://github.com/Kid-Liet/Reg-GAN.

The data transfer bottleneck in Von Neumann architecture owing to the separation between processor and memory hinders the development of high-performance computing. The computing in memory (CIM) concept is widely considered as a promising solution for overcoming this issue. In this article, we present a time-domain CIM (TD-CIM) scheme using spintronics, which can be applied to construct the energy-efficient convolutional neural network (CNN). Basic Boolean logic operations are implemented through recording the bit-line output at different moments. A multi-addend addition mechanism is then introduced based on the TD-CIM circuit, which can eliminate the cascaded full adders. To further optimize the compatibility of TD-CIM circuit for CNN, we also propose a quantization method that transforms floating-point parameters of pre-trained CNN models into fixed-point parameters. Finally, we build a TD-CIM architecture integrating with a highly reconfigurable array of field-free spin-orbit torque magnetic random access memory (SOT-MRAM) and evaluate its benefits for the quantized CNN. By performing digit recognition with the MNIST dataset, we find that the delay and energy are respectively reduced by 1.22.7 times and 2.4×103-1.1×104times compared with STT-CIM and CRAM based on spintronic memory. Finally, the recognition accuracy can reach 98.65% and 91.11% on MNIST and CIFAR10, respectively.

In-memory computing is highly promising to address the processor-memory data transfer bottleneck in current computational paradigm. We firstly propose a timedomain in-memory computing (TIMC) scheme based on highspeed low-power toggle spin torque random access memory (TST-MRAM). The difference of voltage drops of bitline caused by simultaneously-activated bit-cells is reflected to time domain. Reconfigurable logic operations can be performed by utilizing D flip-flops (DFFs) to record the outputs at different moments. In order to demonstrate the advantages of this scheme in terms of speed and energy consumption, an efficient multi-digit addition circuit has been designed and analyzed. Compared with existing IMC schemes, such as spin-transfer torque computing-in-memory (STT-CiM) structure, up to 67% energy saving and 10 times delay improvement can be achieved in the case of four-digit addition by using TIMC scheme.