253 research outputs found
DDC-PIM: Efficient Algorithm/Architecture Co-design for Doubling Data Capacity of SRAM-based Processing-In-Memory
Processing-in-memory (PIM), as a novel computing paradigm, provides
significant performance benefits from the aspect of effective data movement
reduction. SRAM-based PIM has been demonstrated as one of the most promising
candidates due to its endurance and compatibility. However, the integration
density of SRAM-based PIM is much lower than other non-volatile memory-based
ones, due to its inherent 6T structure for storing a single bit. Within
comparable area constraints, SRAM-based PIM exhibits notably lower capacity.
Thus, aiming to unleash its capacity potential, we propose DDC-PIM, an
efficient algorithm/architecture co-design methodology that effectively doubles
the equivalent data capacity. At the algorithmic level, we propose a
filter-wise complementary correlation (FCC) algorithm to obtain a bitwise
complementary pair. At the architecture level, we exploit the intrinsic
cross-coupled structure of 6T SRAM to store the bitwise complementary pair in
their complementary states (), thereby maximizing the data
capacity of each SRAM cell. The dual-broadcast input structure and
reconfigurable unit support both depthwise and pointwise convolution, adhering
to the requirements of various neural networks. Evaluation results show that
DDC-PIM yields about speedup on MobileNetV2 and on
EfficientNet-B0 with negligible accuracy loss compared with PIM baseline
implementation. Compared with state-of-the-art SRAM-based PIM macros, DDC-PIM
achieves up to and improvement in weight density and
area efficiency, respectively.Comment: 14 pages, to be published in IEEE Transactions on Computer-Aided
Design of Integrated Circuits and Systems (TCAD
DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior
We present DiffBIR, which leverages pretrained text-to-image diffusion models
for blind image restoration problem. Our framework adopts a two-stage pipeline.
In the first stage, we pretrain a restoration module across diversified
degradations to improve generalization capability in real-world scenarios. The
second stage leverages the generative ability of latent diffusion models, to
achieve realistic image restoration. Specifically, we introduce an injective
modulation sub-network -- LAControlNet for finetuning, while the pre-trained
Stable Diffusion is to maintain its generative ability. Finally, we introduce a
controllable module that allows users to balance quality and fidelity by
introducing the latent image guidance in the denoising process during
inference. Extensive experiments have demonstrated its superiority over
state-of-the-art approaches for both blind image super-resolution and blind
face restoration tasks on synthetic and real-world datasets. The code is
available at https://github.com/XPixelGroup/DiffBIR
STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training
Large-scale models pre-trained on large-scale datasets have profoundly
advanced the development of deep learning. However, the state-of-the-art models
for medical image segmentation are still small-scale, with their parameters
only in the tens of millions. Further scaling them up to higher orders of
magnitude is rarely explored. An overarching goal of exploring large-scale
models is to train them on large-scale medical segmentation datasets for better
transfer capacities. In this work, we design a series of Scalable and
Transferable U-Net (STU-Net) models, with parameter sizes ranging from 14
million to 1.4 billion. Notably, the 1.4B STU-Net is the largest medical image
segmentation model to date. Our STU-Net is based on nnU-Net framework due to
its popularity and impressive performance. We first refine the default
convolutional blocks in nnU-Net to make them scalable. Then, we empirically
evaluate different scaling combinations of network depth and width, discovering
that it is optimal to scale model depth and width together. We train our
scalable STU-Net models on a large-scale TotalSegmentator dataset and find that
increasing model size brings a stronger performance gain. This observation
reveals that a large model is promising in medical image segmentation.
Furthermore, we evaluate the transferability of our model on 14 downstream
datasets for direct inference and 3 datasets for further fine-tuning, covering
various modalities and segmentation targets. We observe good performance of our
pre-trained model in both direct inference and fine-tuning. The code and
pre-trained models are available at https://github.com/Ziyan-Huang/STU-Net
A-Eval: A Benchmark for Cross-Dataset Evaluation of Abdominal Multi-Organ Segmentation
Although deep learning have revolutionized abdominal multi-organ
segmentation, models often struggle with generalization due to training on
small, specific datasets. With the recent emergence of large-scale datasets,
some important questions arise: \textbf{Can models trained on these datasets
generalize well on different ones? If yes/no, how to further improve their
generalizability?} To address these questions, we introduce A-Eval, a benchmark
for the cross-dataset Evaluation ('Eval') of Abdominal ('A') multi-organ
segmentation. We employ training sets from four large-scale public datasets:
FLARE22, AMOS, WORD, and TotalSegmentator, each providing extensive labels for
abdominal multi-organ segmentation. For evaluation, we incorporate the
validation sets from these datasets along with the training set from the BTCV
dataset, forming a robust benchmark comprising five distinct datasets. We
evaluate the generalizability of various models using the A-Eval benchmark,
with a focus on diverse data usage scenarios: training on individual datasets
independently, utilizing unlabeled data via pseudo-labeling, mixing different
modalities, and joint training across all available datasets. Additionally, we
explore the impact of model sizes on cross-dataset generalizability. Through
these analyses, we underline the importance of effective data usage in
enhancing models' generalization capabilities, offering valuable insights for
assembling large-scale datasets and improving training strategies. The code and
pre-trained models are available at
\href{https://github.com/uni-medical/A-Eval}{https://github.com/uni-medical/A-Eval}
SAM-Med3D
Although the Segment Anything Model (SAM) has demonstrated impressive
performance in 2D natural image segmentation, its application to 3D volumetric
medical images reveals significant shortcomings, namely suboptimal performance
and unstable prediction, necessitating an excessive number of prompt points to
attain the desired outcomes. These issues can hardly be addressed by
fine-tuning SAM on medical data because the original 2D structure of SAM
neglects 3D spatial information. In this paper, we introduce SAM-Med3D, the
most comprehensive study to modify SAM for 3D medical images. Our approach is
characterized by its comprehensiveness in two primary aspects: firstly, by
comprehensively reformulating SAM to a thorough 3D architecture trained on a
comprehensively processed large-scale volumetric medical dataset; and secondly,
by providing a comprehensive evaluation of its performance. Specifically, we
train SAM-Med3D with over 131K 3D masks and 247 categories. Our SAM-Med3D
excels at capturing 3D spatial information, exhibiting competitive performance
with significantly fewer prompt points than the top-performing fine-tuned SAM
in the medical domain. We then evaluate its capabilities across 15 datasets and
analyze it from multiple perspectives, including anatomical structures,
modalities, targets, and generalization abilities. Our approach, compared with
SAM, showcases pronouncedly enhanced efficiency and broad segmentation
capabilities for 3D volumetric medical images. Our code is released at
https://github.com/uni-medical/SAM-Med3D
Efficacy and safety of traditional Chinese medicine external washing in the treatment of postoperative wound of diabetes complicated with anal fistula: Study protocol of a randomized, double-blind, placebo-controlled, multi-center clinical trial
Introduction: Anal fistula is one of the commonest ailments seen by anorectal surgeons as surgery is currently the preferred treatment for it. Diabetes mellitus is a risk factor that can lead to slow wound healing after anal fistula surgery. Because of the large postoperative wound surface of anal fistula, patients with diabetes can have an increased probability of wound infection, which makes it hard to heal. There is an extensive clinical experience for wound healing in traditional Chinese medicine (TCM). The Jiedu Shengji decoction (JSD) is a widely used external washing decoction in clinical practice. However, the current evidence on it is still insufficient. Therefore, we report this carefully designed clinical trial to assess the efficacy and safety of JSD in the treatment of postoperative wounds in diabetic patients with anal fistula.Methods and analysis: This study was designed to be a randomized, double-blind, placebo-controlled, multi-center clinical trial. There were 60 eligible participants who were randomized at a 1:1 ratio to the intervention and placebo groups. Both groups received the same standard treatment. The intervention group was given external washing decoction of TCM (JSD), while the placebo group was given the placebo made of excipients and flavoring agents. The main outcome measures include wound healing, distribution of wound pathogens, levels of inflammatory mediators, and blood glucose. The secondary outcome measures included lipids, the quality of the life evaluation scale (Short-Form Health Survey 36). Assessments were performed before the start of the study, at 1st, 2nd, 3rd, and 4th weeks after the intervention, and at 8th, 12th, and 16th follow-up weeks.Discussion: The clinical study we proposed will be the first randomized, double-blind, placebo-controlled, multi-center clinical trial study to assess the efficacy and safety of TCM external washing (JSD) in the treatment of postoperative wounds in diabetic patients with anal fistula.Ethics and dissemination: The Medical Ethics Committee of Hospital of Chengdu University of Traditional Chinese Medicine has reviewed this study protocol and gave its approval and consent on 17 March, 2022 (Ethical Review Number: 2022KL-018)
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